{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Estimation of COVID-19 Pandemic\n",
    "\n",
    "## Loading Data\n",
    "\n",
    "We will use data on COVID-19 infected individuals, provided by the [Center for Systems Science and Engineering](https://systems.jhu.edu/) (CSSE) at [Johns Hopkins University](https://jhu.edu/). Dataset is available in [this GitHub Repository](https://github.com/CSSEGISandData/COVID-19)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams[\"figure.figsize\"] = (10,3) # make figures larger"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can load the most recent data directly from GitHub using `pd.read_csv`. If for some reason the data is not available, you can always use the copy available locally in the `data` folder - just uncomment the line below that defines `base_url`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_url = \"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/\" # loading from Internet\n",
    "# base_url = \"../../data/COVID/\" # loading from disk\n",
    "infected_dataset_url = base_url + \"time_series_covid19_confirmed_global.csv\"\n",
    "recovered_dataset_url = base_url + \"time_series_covid19_recovered_global.csv\"\n",
    "deaths_dataset_url = base_url + \"time_series_covid19_deaths_global.csv\"\n",
    "countries_dataset_url = base_url + \"../UID_ISO_FIPS_LookUp_Table.csv\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now load the data for infected individuals and see how the data looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>1/22/20</th>\n",
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       "      <th>1/26/20</th>\n",
       "      <th>1/27/20</th>\n",
       "      <th>...</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "      <td>41.15330</td>\n",
       "      <td>20.168300</td>\n",
       "      <td>0</td>\n",
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       "      <td>143174</td>\n",
       "      <td>144079</td>\n",
       "      <td>144847</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Algeria</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>190656</td>\n",
       "      <td>191171</td>\n",
       "      <td>191583</td>\n",
       "      <td>192089</td>\n",
       "      <td>192626</td>\n",
       "      <td>193171</td>\n",
       "      <td>193674</td>\n",
       "      <td>194186</td>\n",
       "      <td>194671</td>\n",
       "      <td>195162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>42.50630</td>\n",
       "      <td>1.521800</td>\n",
       "      <td>0</td>\n",
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       "      <td>15016</td>\n",
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       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>-11.20270</td>\n",
       "      <td>17.873900</td>\n",
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       "      <td>46929</td>\n",
       "      <td>47079</td>\n",
       "      <td>47168</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 590 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Province/State Country/Region       Lat       Long  1/22/20  1/23/20  \\\n",
       "0            NaN    Afghanistan  33.93911  67.709953        0        0   \n",
       "1            NaN        Albania  41.15330  20.168300        0        0   \n",
       "2            NaN        Algeria  28.03390   1.659600        0        0   \n",
       "3            NaN        Andorra  42.50630   1.521800        0        0   \n",
       "4            NaN         Angola -11.20270  17.873900        0        0   \n",
       "\n",
       "   1/24/20  1/25/20  1/26/20  1/27/20  ...  8/20/21  8/21/21  8/22/21  \\\n",
       "0        0        0        0        0  ...   152448   152448   152448   \n",
       "1        0        0        0        0  ...   138132   138790   139324   \n",
       "2        0        0        0        0  ...   190656   191171   191583   \n",
       "3        0        0        0        0  ...    14988    14988    14988   \n",
       "4        0        0        0        0  ...    45583    45817    45945   \n",
       "\n",
       "   8/23/21  8/24/21  8/25/21  8/26/21  8/27/21  8/28/21  8/29/21  \n",
       "0   152583   152660   152722   152822   152960   152960   152960  \n",
       "1   139721   140521   141365   142253   143174   144079   144847  \n",
       "2   192089   192626   193171   193674   194186   194671   195162  \n",
       "3    15002    15003    15014    15016    15025    15025    15025  \n",
       "4    46076    46340    46539    46726    46929    47079    47168  \n",
       "\n",
       "[5 rows x 590 columns]"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "infected = pd.read_csv(infected_dataset_url)\n",
    "infected.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that each row of the table defines the number of infected individuals for each country and/or province, and columns correspond to dates. Similar tables can be loaded for other data, such as number of recovered and number of deaths."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "recovered = pd.read_csv(recovered_dataset_url)\n",
    "deaths = pd.read_csv(deaths_dataset_url)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Making Sense of the Data\n",
    "\n",
    "From the table above the role of province column is not clear. Let's see the different values that are present in `Province/State` column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Australian Capital Territory    1\n",
       "Xinjiang                        1\n",
       "Martinique                      1\n",
       "Guadeloupe                      1\n",
       "French Polynesia                1\n",
       "                               ..\n",
       "Fujian                          1\n",
       "Chongqing                       1\n",
       "Beijing                         1\n",
       "Anhui                           1\n",
       "Turks and Caicos Islands        1\n",
       "Name: Province/State, Length: 87, dtype: int64"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "infected['Province/State'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the names we can deduce that countries like Australia and China have more detailed breakdown by provinces. Let's look for information on China to see the example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Anhui</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Chongqing</td>\n",
       "      <td>China</td>\n",
       "      <td>30.0572</td>\n",
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       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
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       "      <th>61</th>\n",
       "      <td>Fujian</td>\n",
       "      <td>China</td>\n",
       "      <td>26.0789</td>\n",
       "      <td>117.9874</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Gansu</td>\n",
       "      <td>China</td>\n",
       "      <td>35.7518</td>\n",
       "      <td>104.2861</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "      <td>199</td>\n",
       "      <td>199</td>\n",
       "      <td>199</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Guangdong</td>\n",
       "      <td>China</td>\n",
       "      <td>23.3417</td>\n",
       "      <td>113.4244</td>\n",
       "      <td>26</td>\n",
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       "      <td>53</td>\n",
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       "      <td>3043</td>\n",
       "      <td>3046</td>\n",
       "      <td>3055</td>\n",
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       "      <th>64</th>\n",
       "      <td>Guangxi</td>\n",
       "      <td>China</td>\n",
       "      <td>23.8298</td>\n",
       "      <td>108.7881</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
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       "      <td>289</td>\n",
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       "      <td>289</td>\n",
       "      <td>289</td>\n",
       "      <td>289</td>\n",
       "      <td>289</td>\n",
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       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Guizhou</td>\n",
       "      <td>China</td>\n",
       "      <td>26.8154</td>\n",
       "      <td>106.8748</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>147</td>\n",
       "      <td>147</td>\n",
       "      <td>147</td>\n",
       "      <td>147</td>\n",
       "      <td>147</td>\n",
       "      <td>147</td>\n",
       "      <td>147</td>\n",
       "      <td>147</td>\n",
       "      <td>147</td>\n",
       "      <td>147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Hainan</td>\n",
       "      <td>China</td>\n",
       "      <td>19.1959</td>\n",
       "      <td>109.7453</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
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       "      <td>33</td>\n",
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       "      <td>190</td>\n",
       "      <td>190</td>\n",
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       "      <td>190</td>\n",
       "      <td>190</td>\n",
       "      <td>190</td>\n",
       "      <td>190</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Hebei</td>\n",
       "      <td>China</td>\n",
       "      <td>39.5490</td>\n",
       "      <td>116.1306</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>18</td>\n",
       "      <td>...</td>\n",
       "      <td>1317</td>\n",
       "      <td>1317</td>\n",
       "      <td>1317</td>\n",
       "      <td>1317</td>\n",
       "      <td>1317</td>\n",
       "      <td>1317</td>\n",
       "      <td>1317</td>\n",
       "      <td>1317</td>\n",
       "      <td>1317</td>\n",
       "      <td>1317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Heilongjiang</td>\n",
       "      <td>China</td>\n",
       "      <td>47.8620</td>\n",
       "      <td>127.7615</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>21</td>\n",
       "      <td>...</td>\n",
       "      <td>1614</td>\n",
       "      <td>1614</td>\n",
       "      <td>1614</td>\n",
       "      <td>1614</td>\n",
       "      <td>1614</td>\n",
       "      <td>1614</td>\n",
       "      <td>1615</td>\n",
       "      <td>1615</td>\n",
       "      <td>1615</td>\n",
       "      <td>1615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Henan</td>\n",
       "      <td>China</td>\n",
       "      <td>37.8957</td>\n",
       "      <td>114.9042</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>32</td>\n",
       "      <td>83</td>\n",
       "      <td>128</td>\n",
       "      <td>...</td>\n",
       "      <td>1521</td>\n",
       "      <td>1522</td>\n",
       "      <td>1523</td>\n",
       "      <td>1524</td>\n",
       "      <td>1525</td>\n",
       "      <td>1525</td>\n",
       "      <td>1527</td>\n",
       "      <td>1528</td>\n",
       "      <td>1528</td>\n",
       "      <td>1528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>China</td>\n",
       "      <td>22.3000</td>\n",
       "      <td>114.2000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>12049</td>\n",
       "      <td>12052</td>\n",
       "      <td>12057</td>\n",
       "      <td>12062</td>\n",
       "      <td>12069</td>\n",
       "      <td>12074</td>\n",
       "      <td>12077</td>\n",
       "      <td>12094</td>\n",
       "      <td>12100</td>\n",
       "      <td>12107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Hubei</td>\n",
       "      <td>China</td>\n",
       "      <td>30.9756</td>\n",
       "      <td>112.2707</td>\n",
       "      <td>444</td>\n",
       "      <td>444</td>\n",
       "      <td>549</td>\n",
       "      <td>761</td>\n",
       "      <td>1058</td>\n",
       "      <td>1423</td>\n",
       "      <td>...</td>\n",
       "      <td>68287</td>\n",
       "      <td>68289</td>\n",
       "      <td>68289</td>\n",
       "      <td>68289</td>\n",
       "      <td>68289</td>\n",
       "      <td>68289</td>\n",
       "      <td>68290</td>\n",
       "      <td>68290</td>\n",
       "      <td>68290</td>\n",
       "      <td>68290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Hunan</td>\n",
       "      <td>China</td>\n",
       "      <td>27.6104</td>\n",
       "      <td>111.7088</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>24</td>\n",
       "      <td>43</td>\n",
       "      <td>69</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>1181</td>\n",
       "      <td>1181</td>\n",
       "      <td>1181</td>\n",
       "      <td>1181</td>\n",
       "      <td>1181</td>\n",
       "      <td>1181</td>\n",
       "      <td>1181</td>\n",
       "      <td>1181</td>\n",
       "      <td>1181</td>\n",
       "      <td>1181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Inner Mongolia</td>\n",
       "      <td>China</td>\n",
       "      <td>44.0935</td>\n",
       "      <td>113.9448</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>412</td>\n",
       "      <td>412</td>\n",
       "      <td>412</td>\n",
       "      <td>412</td>\n",
       "      <td>412</td>\n",
       "      <td>412</td>\n",
       "      <td>412</td>\n",
       "      <td>412</td>\n",
       "      <td>412</td>\n",
       "      <td>412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Jiangsu</td>\n",
       "      <td>China</td>\n",
       "      <td>32.9711</td>\n",
       "      <td>119.4550</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "      <td>33</td>\n",
       "      <td>47</td>\n",
       "      <td>...</td>\n",
       "      <td>1583</td>\n",
       "      <td>1584</td>\n",
       "      <td>1584</td>\n",
       "      <td>1584</td>\n",
       "      <td>1586</td>\n",
       "      <td>1586</td>\n",
       "      <td>1587</td>\n",
       "      <td>1587</td>\n",
       "      <td>1589</td>\n",
       "      <td>1589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Jiangxi</td>\n",
       "      <td>China</td>\n",
       "      <td>27.6140</td>\n",
       "      <td>115.7221</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>36</td>\n",
       "      <td>72</td>\n",
       "      <td>...</td>\n",
       "      <td>937</td>\n",
       "      <td>937</td>\n",
       "      <td>937</td>\n",
       "      <td>937</td>\n",
       "      <td>937</td>\n",
       "      <td>937</td>\n",
       "      <td>937</td>\n",
       "      <td>937</td>\n",
       "      <td>937</td>\n",
       "      <td>937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Jilin</td>\n",
       "      <td>China</td>\n",
       "      <td>43.6661</td>\n",
       "      <td>126.1923</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>574</td>\n",
       "      <td>574</td>\n",
       "      <td>574</td>\n",
       "      <td>574</td>\n",
       "      <td>574</td>\n",
       "      <td>574</td>\n",
       "      <td>574</td>\n",
       "      <td>574</td>\n",
       "      <td>574</td>\n",
       "      <td>574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Liaoning</td>\n",
       "      <td>China</td>\n",
       "      <td>41.2956</td>\n",
       "      <td>122.6085</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>...</td>\n",
       "      <td>443</td>\n",
       "      <td>443</td>\n",
       "      <td>443</td>\n",
       "      <td>443</td>\n",
       "      <td>443</td>\n",
       "      <td>444</td>\n",
       "      <td>445</td>\n",
       "      <td>446</td>\n",
       "      <td>446</td>\n",
       "      <td>446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Macau</td>\n",
       "      <td>China</td>\n",
       "      <td>22.1667</td>\n",
       "      <td>113.5500</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Ningxia</td>\n",
       "      <td>China</td>\n",
       "      <td>37.2692</td>\n",
       "      <td>106.1655</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Qinghai</td>\n",
       "      <td>China</td>\n",
       "      <td>35.7452</td>\n",
       "      <td>95.9956</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Shaanxi</td>\n",
       "      <td>China</td>\n",
       "      <td>35.1917</td>\n",
       "      <td>108.8701</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>22</td>\n",
       "      <td>35</td>\n",
       "      <td>...</td>\n",
       "      <td>668</td>\n",
       "      <td>668</td>\n",
       "      <td>668</td>\n",
       "      <td>669</td>\n",
       "      <td>669</td>\n",
       "      <td>669</td>\n",
       "      <td>669</td>\n",
       "      <td>669</td>\n",
       "      <td>669</td>\n",
       "      <td>669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Shandong</td>\n",
       "      <td>China</td>\n",
       "      <td>36.3427</td>\n",
       "      <td>118.1498</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>27</td>\n",
       "      <td>46</td>\n",
       "      <td>75</td>\n",
       "      <td>...</td>\n",
       "      <td>923</td>\n",
       "      <td>923</td>\n",
       "      <td>923</td>\n",
       "      <td>923</td>\n",
       "      <td>923</td>\n",
       "      <td>923</td>\n",
       "      <td>923</td>\n",
       "      <td>923</td>\n",
       "      <td>923</td>\n",
       "      <td>924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Shanghai</td>\n",
       "      <td>China</td>\n",
       "      <td>31.2020</td>\n",
       "      <td>121.4491</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>20</td>\n",
       "      <td>33</td>\n",
       "      <td>40</td>\n",
       "      <td>53</td>\n",
       "      <td>...</td>\n",
       "      <td>2420</td>\n",
       "      <td>2432</td>\n",
       "      <td>2436</td>\n",
       "      <td>2445</td>\n",
       "      <td>2451</td>\n",
       "      <td>2454</td>\n",
       "      <td>2462</td>\n",
       "      <td>2466</td>\n",
       "      <td>2471</td>\n",
       "      <td>2476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Shanxi</td>\n",
       "      <td>China</td>\n",
       "      <td>37.5777</td>\n",
       "      <td>112.2922</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>256</td>\n",
       "      <td>256</td>\n",
       "      <td>256</td>\n",
       "      <td>256</td>\n",
       "      <td>256</td>\n",
       "      <td>258</td>\n",
       "      <td>258</td>\n",
       "      <td>259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Sichuan</td>\n",
       "      <td>China</td>\n",
       "      <td>30.6171</td>\n",
       "      <td>102.7103</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>28</td>\n",
       "      <td>44</td>\n",
       "      <td>69</td>\n",
       "      <td>...</td>\n",
       "      <td>1181</td>\n",
       "      <td>1182</td>\n",
       "      <td>1183</td>\n",
       "      <td>1185</td>\n",
       "      <td>1185</td>\n",
       "      <td>1185</td>\n",
       "      <td>1186</td>\n",
       "      <td>1187</td>\n",
       "      <td>1188</td>\n",
       "      <td>1188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Tianjin</td>\n",
       "      <td>China</td>\n",
       "      <td>39.3054</td>\n",
       "      <td>117.3230</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>23</td>\n",
       "      <td>...</td>\n",
       "      <td>458</td>\n",
       "      <td>459</td>\n",
       "      <td>462</td>\n",
       "      <td>463</td>\n",
       "      <td>464</td>\n",
       "      <td>465</td>\n",
       "      <td>466</td>\n",
       "      <td>466</td>\n",
       "      <td>470</td>\n",
       "      <td>472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Tibet</td>\n",
       "      <td>China</td>\n",
       "      <td>31.6927</td>\n",
       "      <td>88.0924</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>China</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Xinjiang</td>\n",
       "      <td>China</td>\n",
       "      <td>41.1129</td>\n",
       "      <td>85.2401</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>980</td>\n",
       "      <td>980</td>\n",
       "      <td>980</td>\n",
       "      <td>980</td>\n",
       "      <td>980</td>\n",
       "      <td>980</td>\n",
       "      <td>980</td>\n",
       "      <td>980</td>\n",
       "      <td>980</td>\n",
       "      <td>980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Yunnan</td>\n",
       "      <td>China</td>\n",
       "      <td>24.9740</td>\n",
       "      <td>101.4870</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "      <td>26</td>\n",
       "      <td>...</td>\n",
       "      <td>1000</td>\n",
       "      <td>1007</td>\n",
       "      <td>1010</td>\n",
       "      <td>1014</td>\n",
       "      <td>1021</td>\n",
       "      <td>1031</td>\n",
       "      <td>1039</td>\n",
       "      <td>1047</td>\n",
       "      <td>1064</td>\n",
       "      <td>1067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Zhejiang</td>\n",
       "      <td>China</td>\n",
       "      <td>29.1832</td>\n",
       "      <td>120.0934</td>\n",
       "      <td>10</td>\n",
       "      <td>27</td>\n",
       "      <td>43</td>\n",
       "      <td>62</td>\n",
       "      <td>104</td>\n",
       "      <td>128</td>\n",
       "      <td>...</td>\n",
       "      <td>1420</td>\n",
       "      <td>1420</td>\n",
       "      <td>1421</td>\n",
       "      <td>1428</td>\n",
       "      <td>1428</td>\n",
       "      <td>1429</td>\n",
       "      <td>1429</td>\n",
       "      <td>1429</td>\n",
       "      <td>1429</td>\n",
       "      <td>1430</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>34 rows × 590 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Province/State Country/Region      Lat      Long  1/22/20  1/23/20  \\\n",
       "58           Anhui          China  31.8257  117.2264        1        9   \n",
       "59         Beijing          China  40.1824  116.4142       14       22   \n",
       "60       Chongqing          China  30.0572  107.8740        6        9   \n",
       "61          Fujian          China  26.0789  117.9874        1        5   \n",
       "62           Gansu          China  35.7518  104.2861        0        2   \n",
       "63       Guangdong          China  23.3417  113.4244       26       32   \n",
       "64         Guangxi          China  23.8298  108.7881        2        5   \n",
       "65         Guizhou          China  26.8154  106.8748        1        3   \n",
       "66          Hainan          China  19.1959  109.7453        4        5   \n",
       "67           Hebei          China  39.5490  116.1306        1        1   \n",
       "68    Heilongjiang          China  47.8620  127.7615        0        2   \n",
       "69           Henan          China  37.8957  114.9042        5        5   \n",
       "70       Hong Kong          China  22.3000  114.2000        0        2   \n",
       "71           Hubei          China  30.9756  112.2707      444      444   \n",
       "72           Hunan          China  27.6104  111.7088        4        9   \n",
       "73  Inner Mongolia          China  44.0935  113.9448        0        0   \n",
       "74         Jiangsu          China  32.9711  119.4550        1        5   \n",
       "75         Jiangxi          China  27.6140  115.7221        2        7   \n",
       "76           Jilin          China  43.6661  126.1923        0        1   \n",
       "77        Liaoning          China  41.2956  122.6085        2        3   \n",
       "78           Macau          China  22.1667  113.5500        1        2   \n",
       "79         Ningxia          China  37.2692  106.1655        1        1   \n",
       "80         Qinghai          China  35.7452   95.9956        0        0   \n",
       "81         Shaanxi          China  35.1917  108.8701        0        3   \n",
       "82        Shandong          China  36.3427  118.1498        2        6   \n",
       "83        Shanghai          China  31.2020  121.4491        9       16   \n",
       "84          Shanxi          China  37.5777  112.2922        1        1   \n",
       "85         Sichuan          China  30.6171  102.7103        5        8   \n",
       "86         Tianjin          China  39.3054  117.3230        4        4   \n",
       "87           Tibet          China  31.6927   88.0924        0        0   \n",
       "88         Unknown          China      NaN       NaN        0        0   \n",
       "89        Xinjiang          China  41.1129   85.2401        0        2   \n",
       "90          Yunnan          China  24.9740  101.4870        1        2   \n",
       "91        Zhejiang          China  29.1832  120.0934       10       27   \n",
       "\n",
       "    1/24/20  1/25/20  1/26/20  1/27/20  ...  8/20/21  8/21/21  8/22/21  \\\n",
       "58       15       39       60       70  ...     1008     1008     1008   \n",
       "59       36       41       68       80  ...     1112     1113     1115   \n",
       "60       27       57       75      110  ...      603      603      603   \n",
       "61       10       18       35       59  ...      780      780      780   \n",
       "62        2        4        7       14  ...      199      199      199   \n",
       "63       53       78      111      151  ...     3001     3007     3012   \n",
       "64       23       23       36       46  ...      289      289      289   \n",
       "65        3        4        5        7  ...      147      147      147   \n",
       "66        8       19       22       33  ...      190      190      190   \n",
       "67        2        8       13       18  ...     1317     1317     1317   \n",
       "68        4        9       15       21  ...     1614     1614     1614   \n",
       "69        9       32       83      128  ...     1521     1522     1523   \n",
       "70        2        5        8        8  ...    12049    12052    12057   \n",
       "71      549      761     1058     1423  ...    68287    68289    68289   \n",
       "72       24       43       69      100  ...     1181     1181     1181   \n",
       "73        1        7        7       11  ...      412      412      412   \n",
       "74        9       18       33       47  ...     1583     1584     1584   \n",
       "75       18       18       36       72  ...      937      937      937   \n",
       "76        3        4        4        6  ...      574      574      574   \n",
       "77        4       17       21       27  ...      443      443      443   \n",
       "78        2        2        5        6  ...       63       63       63   \n",
       "79        2        3        4        7  ...       77       77       77   \n",
       "80        0        1        1        6  ...       18       18       18   \n",
       "81        5       15       22       35  ...      668      668      668   \n",
       "82       15       27       46       75  ...      923      923      923   \n",
       "83       20       33       40       53  ...     2420     2432     2436   \n",
       "84        1        6        9       13  ...      255      255      256   \n",
       "85       15       28       44       69  ...     1181     1182     1183   \n",
       "86        8       10       14       23  ...      458      459      462   \n",
       "87        0        0        0        0  ...        1        1        1   \n",
       "88        0        0        0        0  ...        0        0        0   \n",
       "89        2        3        4        5  ...      980      980      980   \n",
       "90        5       11       16       26  ...     1000     1007     1010   \n",
       "91       43       62      104      128  ...     1420     1420     1421   \n",
       "\n",
       "    8/23/21  8/24/21  8/25/21  8/26/21  8/27/21  8/28/21  8/29/21  \n",
       "58     1008     1008     1008     1008     1008     1008     1008  \n",
       "59     1115     1115     1115     1115     1115     1115     1115  \n",
       "60      603      603      603      603      603      603      603  \n",
       "61      782      782      783      783      784      785      786  \n",
       "62      199      199      199      199      199      199      199  \n",
       "63     3020     3023     3032     3040     3043     3046     3055  \n",
       "64      289      289      289      289      289      289      289  \n",
       "65      147      147      147      147      147      147      147  \n",
       "66      190      190      190      190      190      190      190  \n",
       "67     1317     1317     1317     1317     1317     1317     1317  \n",
       "68     1614     1614     1614     1615     1615     1615     1615  \n",
       "69     1524     1525     1525     1527     1528     1528     1528  \n",
       "70    12062    12069    12074    12077    12094    12100    12107  \n",
       "71    68289    68289    68289    68290    68290    68290    68290  \n",
       "72     1181     1181     1181     1181     1181     1181     1181  \n",
       "73      412      412      412      412      412      412      412  \n",
       "74     1584     1586     1586     1587     1587     1589     1589  \n",
       "75      937      937      937      937      937      937      937  \n",
       "76      574      574      574      574      574      574      574  \n",
       "77      443      443      444      445      446      446      446  \n",
       "78       63       63       63       63       63       63       63  \n",
       "79       77       77       77       77       77       77       77  \n",
       "80       18       18       18       18       18       18       18  \n",
       "81      669      669      669      669      669      669      669  \n",
       "82      923      923      923      923      923      923      924  \n",
       "83     2445     2451     2454     2462     2466     2471     2476  \n",
       "84      256      256      256      256      258      258      259  \n",
       "85     1185     1185     1185     1186     1187     1188     1188  \n",
       "86      463      464      465      466      466      470      472  \n",
       "87        1        1        1        1        1        1        1  \n",
       "88        0        0        0        0        0        0        0  \n",
       "89      980      980      980      980      980      980      980  \n",
       "90     1014     1021     1031     1039     1047     1064     1067  \n",
       "91     1428     1428     1429     1429     1429     1429     1430  \n",
       "\n",
       "[34 rows x 590 columns]"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "infected[infected['Country/Region']=='China']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pre-processing the Data \n",
    "\n",
    "We are not interested in breaking countries down to further territories, thus we would first get rid of this breakdown and add information on all territories together, to get info for the whole country. This can be done using `groupby`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>1/22/20</th>\n",
       "      <th>1/23/20</th>\n",
       "      <th>1/24/20</th>\n",
       "      <th>1/25/20</th>\n",
       "      <th>1/26/20</th>\n",
       "      <th>1/27/20</th>\n",
       "      <th>1/28/20</th>\n",
       "      <th>1/29/20</th>\n",
       "      <th>...</th>\n",
       "      <th>8/20/21</th>\n",
       "      <th>8/21/21</th>\n",
       "      <th>8/22/21</th>\n",
       "      <th>8/23/21</th>\n",
       "      <th>8/24/21</th>\n",
       "      <th>8/25/21</th>\n",
       "      <th>8/26/21</th>\n",
       "      <th>8/27/21</th>\n",
       "      <th>8/28/21</th>\n",
       "      <th>8/29/21</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country/Region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>33.93911</td>\n",
       "      <td>67.709953</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>152448</td>\n",
       "      <td>152448</td>\n",
       "      <td>152448</td>\n",
       "      <td>152583</td>\n",
       "      <td>152660</td>\n",
       "      <td>152722</td>\n",
       "      <td>152822</td>\n",
       "      <td>152960</td>\n",
       "      <td>152960</td>\n",
       "      <td>152960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>41.15330</td>\n",
       "      <td>20.168300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>138132</td>\n",
       "      <td>138790</td>\n",
       "      <td>139324</td>\n",
       "      <td>139721</td>\n",
       "      <td>140521</td>\n",
       "      <td>141365</td>\n",
       "      <td>142253</td>\n",
       "      <td>143174</td>\n",
       "      <td>144079</td>\n",
       "      <td>144847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>28.03390</td>\n",
       "      <td>1.659600</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>190656</td>\n",
       "      <td>191171</td>\n",
       "      <td>191583</td>\n",
       "      <td>192089</td>\n",
       "      <td>192626</td>\n",
       "      <td>193171</td>\n",
       "      <td>193674</td>\n",
       "      <td>194186</td>\n",
       "      <td>194671</td>\n",
       "      <td>195162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>42.50630</td>\n",
       "      <td>1.521800</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>14988</td>\n",
       "      <td>14988</td>\n",
       "      <td>14988</td>\n",
       "      <td>15002</td>\n",
       "      <td>15003</td>\n",
       "      <td>15014</td>\n",
       "      <td>15016</td>\n",
       "      <td>15025</td>\n",
       "      <td>15025</td>\n",
       "      <td>15025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>-11.20270</td>\n",
       "      <td>17.873900</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>45583</td>\n",
       "      <td>45817</td>\n",
       "      <td>45945</td>\n",
       "      <td>46076</td>\n",
       "      <td>46340</td>\n",
       "      <td>46539</td>\n",
       "      <td>46726</td>\n",
       "      <td>46929</td>\n",
       "      <td>47079</td>\n",
       "      <td>47168</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 588 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Lat       Long  1/22/20  1/23/20  1/24/20  1/25/20  \\\n",
       "Country/Region                                                            \n",
       "Afghanistan     33.93911  67.709953        0        0        0        0   \n",
       "Albania         41.15330  20.168300        0        0        0        0   \n",
       "Algeria         28.03390   1.659600        0        0        0        0   \n",
       "Andorra         42.50630   1.521800        0        0        0        0   \n",
       "Angola         -11.20270  17.873900        0        0        0        0   \n",
       "\n",
       "                1/26/20  1/27/20  1/28/20  1/29/20  ...  8/20/21  8/21/21  \\\n",
       "Country/Region                                      ...                     \n",
       "Afghanistan           0        0        0        0  ...   152448   152448   \n",
       "Albania               0        0        0        0  ...   138132   138790   \n",
       "Algeria               0        0        0        0  ...   190656   191171   \n",
       "Andorra               0        0        0        0  ...    14988    14988   \n",
       "Angola                0        0        0        0  ...    45583    45817   \n",
       "\n",
       "                8/22/21  8/23/21  8/24/21  8/25/21  8/26/21  8/27/21  8/28/21  \\\n",
       "Country/Region                                                                  \n",
       "Afghanistan      152448   152583   152660   152722   152822   152960   152960   \n",
       "Albania          139324   139721   140521   141365   142253   143174   144079   \n",
       "Algeria          191583   192089   192626   193171   193674   194186   194671   \n",
       "Andorra           14988    15002    15003    15014    15016    15025    15025   \n",
       "Angola            45945    46076    46340    46539    46726    46929    47079   \n",
       "\n",
       "                8/29/21  \n",
       "Country/Region           \n",
       "Afghanistan      152960  \n",
       "Albania          144847  \n",
       "Algeria          195162  \n",
       "Andorra           15025  \n",
       "Angola            47168  \n",
       "\n",
       "[5 rows x 588 columns]"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "infected = infected.groupby('Country/Region').sum()\n",
    "recovered = recovered.groupby('Country/Region').sum()\n",
    "deaths = deaths.groupby('Country/Region').sum()\n",
    "\n",
    "infected.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that due to using `groupby` all DataFrames are now indexed by Country/Region. We can thus access the data for a specific country by using `.loc`:|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "infected.loc['US'][2:].plot()\n",
    "recovered.loc['US'][2:].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Note** how we use `[2:]` to remove first two elements of a sequence that contain geolocation of a country. We can also drop those two columns altogether:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "infected.drop(columns=['Lat','Long'],inplace=True)\n",
    "recovered.drop(columns=['Lat','Long'],inplace=True)\n",
    "deaths.drop(columns=['Lat','Long'],inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Investigating the Data\n",
    "\n",
    "Let's now switch to investigating a specific country. Let's create a frame that contains the data on infections indexed by date:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>infected</th>\n",
       "      <th>recovered</th>\n",
       "      <th>deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-22</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-23</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-24</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-25</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-26</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-25</th>\n",
       "      <td>38223029</td>\n",
       "      <td>0</td>\n",
       "      <td>632272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-26</th>\n",
       "      <td>38384360</td>\n",
       "      <td>0</td>\n",
       "      <td>633564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-27</th>\n",
       "      <td>38707294</td>\n",
       "      <td>0</td>\n",
       "      <td>636720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-28</th>\n",
       "      <td>38760363</td>\n",
       "      <td>0</td>\n",
       "      <td>637254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-29</th>\n",
       "      <td>38796746</td>\n",
       "      <td>0</td>\n",
       "      <td>637531</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>586 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            infected  recovered  deaths\n",
       "2020-01-22         1          0       0\n",
       "2020-01-23         1          0       0\n",
       "2020-01-24         2          0       0\n",
       "2020-01-25         2          0       0\n",
       "2020-01-26         5          0       0\n",
       "...              ...        ...     ...\n",
       "2021-08-25  38223029          0  632272\n",
       "2021-08-26  38384360          0  633564\n",
       "2021-08-27  38707294          0  636720\n",
       "2021-08-28  38760363          0  637254\n",
       "2021-08-29  38796746          0  637531\n",
       "\n",
       "[586 rows x 3 columns]"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def mkframe(country):\n",
    "    df = pd.DataFrame({ 'infected' : infected.loc[country] ,\n",
    "                        'recovered' : recovered.loc[country],\n",
    "                        'deaths' : deaths.loc[country]})\n",
    "    df.index = pd.to_datetime(df.index)\n",
    "    return df\n",
    "\n",
    "df = mkframe('US')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's compute the number of new infected people each day. This will allow us to see the speed at which pandemic progresses. The easiest day to do it is to use `diff`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['ninfected'] = df['infected'].diff()\n",
    "df['ninfected'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see high fluctuations in data. Let's look closer at one of the months:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[(df.index.year==2020) & (df.index.month==7)]['ninfected'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It clearly looks like there are weekly fluctuations in data. Because we want to be able to see the trends, it makes sense to smooth out the curve by computing running average (i.e. for each day we will compute the average value of the previous several days):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6hl10R8EfCwnwBaCgrOq8Y08U2SmrdGD2MhEbEuC2Gvx8zPSPqVl+mHyswG23KyLSVBoUshYuXMjw4cMJCgoiIiKCGTNmsG/fvjpjDMPggQceIDo6Gn9/fyZMmMCuXbvqjLHb7cybN4+wsDACAwO5+uqrycjIqDOmoKCA2bNnY7VasVqtzJ49m8LCwjpj0tLSmD59OoGBgYSFhXH33XdTWVn3k7edO3cyfvx4/P39iYmJ4aGHHtLSAxERadFqz8dKcPNSQQCrf81MVmH5+UPW4ZM1SwVjQ/zddl5YraG1SwYVskSkBWrQX8T169dz5513kpSUxJo1a6iuriYhIYHS0lLXmH/+85888cQTPP3002zdupWoqCgmTZpEcXGxa8z8+fNZvnw5y5YtY+PGjZSUlDBt2jQcDodrzKxZs0hJSWHVqlWsWrWKlJQUZs+e7TrucDiYOnUqpaWlbNy4kWXLlvH+++9zzz33uMYUFRUxadIkoqOj2bp1K0899RSLFi3iiSeeuKAnS0RExNPS88vYnVWElwmu6BPh9tuvnckqrMdywcO5tUsF679xcX0NjVPIEpGWq0FrDFatWlXn+1deeYWIiAiSk5O57LLLMAyDf//73/zlL3/huuuuA+DVV18lMjKSN998k9tvvx2bzcbixYt5/fXXufLKKwF44403iI2N5YsvvmDy5Mns2bOHVatWkZSUxMiRIwF48cUXGT16NPv27aN3796sXr2a3bt3k56eTnR0NACPP/44c+bM4ZFHHiE4OJilS5dSUVHBkiVLsFgsxMfHs3//fp544gkWLFhwxlazdrsdu93u+r6oSJ2NRESk+Vizu2ap4PAuoXRoZ3H77bc/dU5WYT2WC9aej9U1zH3nY9W6JK49AHuziyixV9POzcsiRUQa00XN7dtsNXtjhIbWbF545MgRsrOzSUhIcI2xWCyMHz+eTZs2AZCcnExVVVWdMdHR0cTHx7vGJCYmYrVaXQELYNSoUVit1jpj4uPjXQELYPLkydjtdpKTk11jxo8fj8ViqTMmMzOTo0ePnvExLVy40LVE0Wq1Ehsbe8HPj4iIiLttOJALwBV93T+LBdC+ATNZjdH0olZHqz8x7f1xGvBdeqHbb19EpDFdcMgyDIMFCxYwbtw44uPjAcjOrlkjHhkZWWdsZGSk61h2dja+vr6EhIScc0xExOkvHhEREXXG/Ph+QkJC8PX1PeeY2u9rx/zY/fffj81mc32lp6ef55kQERFpGvZqB5sP1+xfdWlP93YVrFU7k1VUUU21w3nOsYdza87JaoyZLNCSQRFpuS547v2uu+5ix44dbNy48bRjP16GZxjGeXeB//GYM413x5japhdnq8disdSZ+RIREWkuvj1WSHmVg7B2FvpEBTXKfbQ/1fgCaoJWaKDvGcfZqx2kn2qv3i3M/edkQU3I+vi7TIUsEWlxLmgma968eXz88cd89dVXdOrUyXV5VFRNl6MfzxLl5OS4ZpCioqKorKykoKDgnGNOnDhx2v3m5ubWGfPj+ykoKKCqquqcY3JycoDTZ9tERESau40Ha5YKXtoz7LwfXl4ob7MXQafOfyo4x5LBfdnFOJwG7QN8iAxunA8na2eyvk0rwOlUZ2ARaTkaFLIMw+Cuu+7igw8+4Msvv6Rr1651jnft2pWoqCjWrFnjuqyyspL169czZswYAIYOHYqPj0+dMVlZWaSmprrGjB49GpvNxpYtW1xjNm/ejM1mqzMmNTWVrKws15jVq1djsVgYOnSoa8yGDRvqtHVfvXo10dHRdOnSpSEPXURExOM2HcoDYFyPsEa9n/aB529+kXq8pjHUgBhrowW+PlFB+PuYKa6o5kBOSaPch4hIY2hQyLrzzjt54403ePPNNwkKCiI7O5vs7GzKy2uWC5hMJubPn8+jjz7K8uXLSU1NZc6cOQQEBDBr1iwArFYrt912G/fccw9r165l+/bt3HzzzQwYMMDVbbBv375MmTKFuXPnkpSURFJSEnPnzmXatGn07t0bgISEBPr168fs2bPZvn07a9eu5d5772Xu3LkEBwcDNW3gLRYLc+bMITU1leXLl/Poo4+etbOgiIhIc+VwGuzJqgk2gzu3b9T7au9//uYXO4/XNL/qH21ttDq8zV6u2azEQycb7X5ERNytQSHr2WefxWazMWHCBDp27Oj6evvtt11j7rvvPubPn88dd9zBsGHDOH78OKtXryYo6Pu1408++SQzZsxg5syZjB07loCAAD755BPMZrNrzNKlSxkwYAAJCQkkJCQwcOBAXn/9dddxs9nMihUr8PPzY+zYscycOZMZM2awaNEi1xir1cqaNWvIyMhg2LBh3HHHHSxYsIAFCxZc0JMlIiLiKUfzSqmocuLn40WXDo3TaKJWfdq4p54KWQNiGi9kAYw9NWu38WBeo96PiIg7mYzaThByRkVFRVitVmw2m2uGTEREpKl9uiOTu97czqDY9nx059hGva95b23nk+8y6R4eyPRB0fzuip51VoBUVjuJ/9vnVDqcbPjDRDp3CGi0WnZkFHL1098QZPFm+18n4W2+qN1nREQuSn2zgf5SiYiItAC1SwX7dWz8D/xqOwweyi3l318cYNuPuvvtP1FMpcNJsJ83saH+jVpL/2grVn8fiu3V7Dg1eyYi0twpZImIiLQAe7KKAejXsXFat/9QSIBPne8/Tsms8/32tJrQNaBT4zW9qGX2MjG6WwcAvjmg87JEpGVQyBIREWkBamey+jbBTJY1oO7eWCt3ZtXZmHjjwZqwUxt+GtvIbqFATSt3EZGWQCFLRESkmcu2VZBlqwCgTxOErB/OTQX4mskrrXS1j692OL9vJd8zvNFrARgc2x6A7zJs6FRyEWkJFLJERESaudeTjgIwvEsI7U5tFNyY+vxgSeJPh3YC4LXEY0BN6/biimqC/bwbvbNgrX7RwfiYTeSXVpKeX94k9ykicjEa/y+1iIiIXLCyymreSEoD4LZx3ZrkPsd0D+OF2UPpFx2MvdrJa4nH+GLPCTYeOMl7yemuMWavptlz0uJtpl/HYL7LsJGSUdio3QxFRNxBM1kiIiLN2Mqd2djKq4jrEMCkfpFNdr8J/aPoFBJA9/B2XNm35n5vXryZD081wZjYp2mWCtaqXTKYklbYpPcrInIhFLJERESasSMnSwC4rGd4k80c/dhdl/fAx2zCx2xiRJdQHrtuANdf0qlJaxjcuT0AKelqfiEizZ+WC4qIiDRjJ4rsAERZ/TxWw+DY9iT/v0lYvL2weJs9UsOwuJoOgynpheQUVxAR5LnnQ0TkfDSTJSIi0sSqHE4eX72P/3118Lxjc4prQlZ4kKWxyzqnYD8fjwUsgNjQAAbHtsdpwKffZXmsDhGR+lDIEhERaUIVVQ5+/do2nvryIP/6fB95JfZzjs8pqmndHhmsmZtrh8QA8FHKcQ9XIiJybgpZIiIiTaSiysHc17bx1b5c12VH88rOeZ3amawID89kNQdTB3bE7GXiuwwbR0+WerocEZGzUsgSERFpAvZqB7e/nszXB04S4GvGx1zTxOJY3tnDQmW1k/zSSkAzWQBh7SwMjQsBIPFwnoerERE5O4UsERGRRlZZ7eSON75l/f5c/Hy8eHnOcH46NBY490xW7qmlhD5mEyEBPk1Sa3M3oktNA4ytR/M9XImIyNkpZImIiDSiKoeTu978lrV7c7B4e/HyrcMZ1a0DXU5tqHuumawTp87Higjyw2TyTPv25mZ4V4UsEWn+FLJEREQaSbXDye+WbWf17hP4envx4i3DGNMjDIC4DoHAuWeyck61b48I1vlYtS7p3B4vE6Tnl/Ovz/eSkl7o6ZJERE6jkCUiItJIHl6xh5U7s/E1e/H87KFc1ivcdaxL2PlnsnKKa2eyFLJqBfn50DsqGID/fXWIX7yyBXu1w8NViYjUpZAlIiLSCFLSC3k18SgAT80awsTeEXWOdw6tCVmFZVW8sy0dW3nVabdxQu3bz+jSnmGufxeUVbF2T44HqxEROZ1CloiIiJsZhsH/fbgTw4DrhsQwuX/UaWMCfL2xeNe8DN/33g5+/dq208bULhdUyKrrzgk9WHjdAG4cVtM85J1t6R6uSESkLoUsERERN0s8nEfq8SICfM38eWrfs46Lj7G6/r35SD7JxwrqHD9eWA5AuJYL1mEN8OGmEZ35zYTuAGzYn0uWrdzDVYmIfE8hS0RExM1eTzwGwLVDYghrd/aAdOfE7lzZN5Lxp87V+s/aA1RWOwHYmWFj06GavaAGdrKe9Tbasq5hgYzoEorTgA++Pe7pckREXBSyRERE3CjbVsHq3ScAuGV0l3OOvbxPJC/dOoz/N60fJlPNjMxV/9nAsbxSHvp0F1AT1PqcavQgp5s5/Pslg4ZheLgaEZEaClkiIiJu9OaWNBxOgxFdQ+kdFVSv6/SIaMfTN11CWDtfDuWWMvW/G9l6tAA/Hy/um9K7kStu2X4yIIpAXzPH8srYfER7Z4lI86CQJSIi4iZVDidvbUkDYPaouAZdd+rAjnwybxxh7XwpsVcD8M+fDqKj1d/tdbYmAb7eTB8UDcDrScc8XI2ISA2FLBERETf5fFc2ucV2woMsZ+woeD4drf48P3sYA2Ks/H1GPFefCg9ybreO6QLAZzuzSM8/++bOIiJNRSFLRETEDQzD4KWvjwBw04jO+Hpf2Evs0LgQPpk3rsEzYW1Z347BXNozDKcBL39zxNPliIgoZImIiLhD4uE8UtILsXh7KSB5wNxLuwHwztYzb+wsItKUFLJERETc4Nl1hwCYOSxW+1p5wKU9w+gTFURppcN1XpyIiDtUO5wUllU26DoKWSIiIhcpv7SSrw+cBL6fUZGmZTKZuG1cVwBe+eaIa78xEZELZRgG/117gOGPfMElf1/Dc+sPsbWeXUwVskRERC5S4qlNg3tFtqNzhwAPV9N2XT04mvAgCyeK7Hyx54SnyxGRFi7pcD5PrNlPQVkVTgMe+2wvv1iytV7XVcgSERG5SN8cqpnFGtsjzMOVtG0WbzM3DO0EwLKt6R6uRkRauqe/OgDAjcNi+b+pffH2MtEh0Lde121wyNqwYQPTp08nOjoak8nEhx9+WOf4nDlzMJlMdb5GjRpVZ4zdbmfevHmEhYURGBjI1VdfTUZGRp0xBQUFzJ49G6vVitVqZfbs2RQWFtYZk5aWxvTp0wkMDCQsLIy7776bysq66yV37tzJ+PHj8ff3JyYmhoceekg7wouIiFttOngqZHVXyPK0G4fHAvD1gVwyCtTOXUQuzKrULL45mIe3l4m7r+zJry7txo4HElj3hwn1un6DQ1ZpaSmDBg3i6aefPuuYKVOmkJWV5fpauXJlnePz589n+fLlLFu2jI0bN1JSUsK0adNwOByuMbNmzSIlJYVVq1axatUqUlJSmD17tuu4w+Fg6tSplJaWsnHjRpYtW8b777/PPffc4xpTVFTEpEmTiI6OZuvWrTz11FMsWrSIJ554oqEPW0RE5IwyCso4mleG2cvEyG6hni6nzYvrEMiY7h0wDFwt9UVE6qvK4eTFDYe5883tAPx8ZGdi2tdsCh/g643JZKrX7Xg39I6vuuoqrrrqqnOOsVgsREWdeRNGm83G4sWLef3117nyyisBeOONN4iNjeWLL75g8uTJ7Nmzh1WrVpGUlMTIkSMBePHFFxk9ejT79u2jd+/erF69mt27d5Oenk50dM1mjY8//jhz5szhkUceITg4mKVLl1JRUcGSJUuwWCzEx8ezf/9+nnjiCRYsWHDGJ8lut2O3213fFxUVNfQpEhGRNuT1xGMADO0cQpCfj4erEYA7JvRg06E8Xk86xs9HdqZnZJCnSxKRFqCy2snM5xNJSS8E4PpLOvH/pvW7oNtqlHOy1q1bR0REBL169WLu3Lnk5OS4jiUnJ1NVVUVCQoLrsujoaOLj49m0aRMAiYmJWK1WV8ACGDVqFFartc6Y+Ph4V8ACmDx5Mna7neTkZNeY8ePHY7FY6ozJzMzk6NGjZ6x94cKFriWKVquV2NjYi39CRESkVTpRVMGSTUcB+O2E7p4tRlzG9QxjUr9IHE6Dv360S6cJiMg5VTmc7D9RzGuJR0lJLyTIz5uF1w3gXz8diLf5wuKS20PWVVddxdKlS/nyyy95/PHH2bp1K5dffrlrdig7OxtfX19CQkLqXC8yMpLs7GzXmIiIiNNuOyIios6YyMjIOsdDQkLw9fU955ja72vH/Nj999+PzWZzfaWn68RZERE5s8Ubj2CvdjI0LoQJvcM9XY78wP+b2g8/Hy8SD+fx1ha9lovI2f3xvR0kPLmBh1fsAeDPP+nLTSM64+VVv6WBZ9Lg5YLnc+ONN7r+HR8fz7Bhw4iLi2PFihVcd911Z72eYRh1lu+daSmfO8bUfpp1tvWUFoulzsyXiIjI2aw91Sb8tnFd671OX5pG5w4B/GFyH/7+6W4eWbGbnwyIon1A/bqCiUjbkXysgA+2H3d93ycqiJnDLn4lW6O3cO/YsSNxcXEcOFDTAjEqKorKykoKCgrqjMvJyXHNMkVFRXHixOn7W+Tm5tYZ8+PZqIKCAqqqqs45pnbp4o9nuERERBrieGE5h3JL8TKpdXtzNWdMF3pFtqO00sGq1DOvYBGRtu2xz2pmr64ZHM3/Zl3Ca78cgfkiZrBqNXrIysvLIz09nY4dOwIwdOhQfHx8WLNmjWtMVlYWqampjBkzBoDRo0djs9nYsmWLa8zmzZux2Wx1xqSmppKVleUas3r1aiwWC0OHDnWN2bBhQ5227qtXryY6OpouXbo02mMWEZHWb+OBXAAGx7bH6q+GF82R2cvEjCExAHyyI9PD1YhIc5N63MbWowX4mr24/6q+TB3YkYhgP7fcdoNDVklJCSkpKaSkpABw5MgRUlJSSEtLo6SkhHvvvZfExESOHj3KunXrmD59OmFhYVx77bUAWK1WbrvtNu655x7Wrl3L9u3bufnmmxkwYICr22Dfvn2ZMmUKc+fOJSkpiaSkJObOncu0adPo3bs3AAkJCfTr14/Zs2ezfft21q5dy7333svcuXMJDg4GatrAWywW5syZQ2pqKsuXL+fRRx89a2dBERGR+tpwoGZvrEt76lys5mz6wJoGWYmH8sgprvBwNSLSnLx9atPyhP6RRFndE65qNThkbdu2jSFDhjBkyBAAFixYwJAhQ/jrX/+K2Wxm586dXHPNNfTq1Ytbb72VXr16kZiYSFDQ9+1Tn3zySWbMmMHMmTMZO3YsAQEBfPLJJ5jNZteYpUuXMmDAABISEkhISGDgwIG8/vrrruNms5kVK1bg5+fH2LFjmTlzJjNmzGDRokWuMVarlTVr1pCRkcGwYcO44447WLBgAQsWLLigJ0tERARqzu+t3YD4sl5aKticxYYGMDi2PU4DPtupJYMiUqOiysGHKTXnYtVuYu5OJkN9Tc+pqKgIq9WKzWZzzZCJiEjbdvRkKRMWrcPX24vUBybj693oq+/lIizeeIS/f7qbYXEhvPfbMZ4uR0SagTc3p/Hn5TuJae/P1/dNrHcnwfpmA70qiIiINFDtRpX9o4MVsFqAqQM6YjLBtmMFZBaWe7ocEfGwymon//vqIAC/HNf1olq1n41eGURERBqoNmQN6tTeo3VI/URZ/RjeJRSAFTuyzjNaRFqzbUfz+c0byRwvLCciyMLPR3ZulPtRyBIREWmg7zIKgZrOgtIyTB9U0wBjZapClkhblWUrZ9ZLm/lyb82WTgsm9cLPx3yea10Yt29GLCIi0ppVVjvZlVkEKGS1JBN713SB3JFho9ReTaBFb4FE2ppn1x2istrJgBgrf53ezzXD3Rg0kyUiItIAu7OKqKx2YvX3Ia5DgKfLkXrqFBJATHt/HE6D5GMFni5HRJpYlq2cZVtqWrbf/5M+jRqwQCFLRESkQWqXmYzsGqo9F1uYUd06AJB0OM/DlYhIU3st8RiVDicjuoQypnvjb72hkCUiItIAq3fV7LU0uX+UhyuRhhrZreaT681H8j1ciYg0pYoqB29tSQPgtku7Nsl9KmSJiIjUU1peGXuzizF7mbi8T4Sny5EGGtW1ZiZrR0YhpfZqD1cjIk3l/W8zKCyrIqa9P1f2jWyS+1TIEhERqafVu2tmsUZ0CSUk0NfD1UhDxYb6E9chgCqH4Vr2KSKt17G8UjYdPMnClXsB+MXYLpgbYU+sM1HIEhERqaf1+3MBuKKvZrFaIpPJxPSBNa3cP/4u08PViEhj2pVpY+Kidcx6aTMl9mpGdAnl1jFdmuz+FbJERETqwV7tYOvRmnN5Lu0Z7uFq5ELV7pe1fl8utvIqD1cjIo3l4+8ycRo1/+4WHsjTs4bgY2666KOQJSIiUg/b0wqpqHIS1s5Cr8h2ni5HLlDvqCB6Rbaj0uFkze4Tni5HRBpJ7e/307OG8OU9E4gI9mvS+1fIEhERqYdNB08CMKZ7B7Vub+GmxHcE4CudlyXSKh3KLeFwbik+ZhPje3lm5YFCloiIyHk4nQZf7as5H2tcj8bfX0UaV21nyA37c6lyOD1cjYi4U1FFFf/+4gAAo7uHEeTn45E6FLJERETO499f7GfncRu+Zi8u89CnouI+A2OsdAj0pdheTfKxAk+XIyJudNuSrXxyqrHNTcNjPVaHQpaIiMg57Mq08d8vDwLwyLXxRFmbdl2/uJ+X1/dLiL7apyWDIq3F8cJyth4twMsEr982gqsGdPRYLQpZIiIi57ByZxYACf0iuWGY5z4VFfeaeGrJ4Ifbj1NR5fBwNSLiDrXnzg7s1N7jXWAVskRERM7h8101HaqmDvTcJ6Lifgn9I+lo9eNEkZ13t6V7uhwRcYNNh/IAGNujg4crUcgSERE5q0O5JRzMKcHHbHLNfEjrYPE2c8eE7gA8s+4Q1WqAIdKilVc62HSoZiZrbHfPNyhSyBIRETkDp9PghfWHARjVrQPBHupQJY1n5vBYrP4+ZNkq2Hnc5ulyROQCfZRynPgHPudEkR2LtxeXxIV4uiSFLBERkTN5ZOUe3j61jOzmUXEerkYag8XbzMiuoQAkHc73cDUicqE++S4Lh9MA4KYRnfHzMXu4IoUsERGR05TYq3kt8SgA//zpQCb3j/JsQdJoRnWrOXcj6XCehysRkQu1K7NmJnrZr0fxwNX9PVxNDYUsERGRH9l44CRVDoO4DgHcMLSTp8uRRlQbsrYdzdd5WSIt0MkSO1m2CgDiY6weruZ7ClkiIiI/8tXemr2TJvaOwGQyebgaaUx9ooKw+vtQWukgNbPI0+WISAPtOvV72y0skHYWbw9X8z2FLBERkR8wDMO1Qe3l6ijY6nl5mRjVrea8rLc2p3m4GhFpqNRTTWv6N6NZLFDIEhERqSMlvZCcYjsBvmZGnnrzLa3bry/rBsC7yensyy72cDUiUh+GYfDNwZP86/N9AMRHB3u4oroUskRERH7g3eQMACb1i8Ti7fkOVdL4hsaFclV8FE4DFq3e5+lyROQ8DMNg0ep9/Pylza7LBmgmS0REpHkqr3TwSUomADcOi/VwNdKU7knoBcAXe06Qnl/m4WpE5Fw+TDnO/746BMDIrqHcOCyWEV2b18oDhSwREZFTPkvNotheTWyov6vrnLQNPSKCGNcjDMOApTo3S6RZ+2JPzXmzt43rytu3j+YfPx2It7l5xZrmVY2IiIgHfb4rG4DrhnTCy0tdBdua2aNrNp1+e2saxRVVHq5GRM5mT1ZNR8FLe4Z5uJKza3DI2rBhA9OnTyc6OhqTycSHH35Y57hhGDzwwANER0fj7+/PhAkT2LVrV50xdrudefPmERYWRmBgIFdffTUZGRl1xhQUFDB79mysVitWq5XZs2dTWFhYZ0xaWhrTp08nMDCQsLAw7r77biorK+uM2blzJ+PHj8ff35+YmBgeeughDMNo6MMWEZFWzl7tYOOBkwBc2TfSw9WIJ1zRJ4JuYYEUlFXx5JoDni5HRM6gvNLB0ZOlAPTr2LyaXfxQg0NWaWkpgwYN4umnnz7j8X/+85888cQTPP3002zdupWoqCgmTZpEcfH33Xrmz5/P8uXLWbZsGRs3bqSkpIRp06bhcDhcY2bNmkVKSgqrVq1i1apVpKSkMHv2bNdxh8PB1KlTKS0tZePGjSxbtoz333+fe+65xzWmqKiISZMmER0dzdatW3nqqadYtGgRTzzxREMftoiItHLbjhZQWukgrJ2F/s2sS5U0DW+zFw9c3R+AJZuOqNOgSDO070QxTgM6BPoSHmTxdDlnZ1wEwFi+fLnre6fTaURFRRmPPfaY67KKigrDarUazz33nGEYhlFYWGj4+PgYy5Ytc405fvy44eXlZaxatcowDMPYvXu3ARhJSUmuMYmJiQZg7N271zAMw1i5cqXh5eVlHD9+3DXmrbfeMiwWi2Gz2QzDMIxnnnnGsFqtRkVFhWvMwoULjejoaMPpdNbrMdpsNgNw3aaIiLROf/9klxH3x0+Ne95J8XQp4mG/enWrEffHT42HP93l6VJE5Efe3HzMiPvjp8bPX0w6/+BGUN9s4NZzso4cOUJ2djYJCQmuyywWC+PHj2fTpk0AJCcnU1VVVWdMdHQ08fHxrjGJiYlYrVZGjhzpGjNq1CisVmudMfHx8URHR7vGTJ48GbvdTnJysmvM+PHjsVgsdcZkZmZy9OjRMz4Gu91OUVFRnS8REWndnE6D1btPADCxtzYgbuuuvyQGgM93ndApBiLNTO35WH07Bnm4knNza8jKzq45YTgysu5a9sjISNex7OxsfH19CQkJOeeYiIjTX+QiIiLqjPnx/YSEhODr63vOMbXf1475sYULF7rOA7NarcTGqoWviEhrt3ZvDmn5ZQT5eTOhd7inyxEPu6xXOBZvL9Lyy9iTpSWDIs3J9yGreS/rbpTugiZT3Y5MhmGcdtmP/XjMmca7Y0ztJ1Jnq+f+++/HZrO5vtLT089Zt4iItHyLNx4GYNaIzgRavD1cjXhagK8343vVhO1VqVkerkZEahWWVZKSXgjAoNj2Hq3lfNwasqKiooDTZ4lycnJcM0hRUVFUVlZSUFBwzjEnTpw47fZzc3PrjPnx/RQUFFBVVXXOMTk5NX31fzzDVctisRAcHFznS0REWq8jJ0tJOpyP2cvErWO6eLocaSamDuwIwCvfHCXbVuHhakQEYFVqNlUOgz5RQXQPb+fpcs7JrSGra9euREVFsWbNGtdllZWVrF+/njFjxgAwdOhQfHx86ozJysoiNTXVNWb06NHYbDa2bNniGrN582ZsNludMampqWRlff8J0+rVq7FYLAwdOtQ1ZsOGDXXauq9evZro6Gi6dOnizocuIiIt1Naj+QAM7RxCdHt/D1cjzcW0gdEMim1Psb2a//sw1dPliAjwUUomANcMjvFwJefX4JBVUlJCSkoKKSkpQE2zi5SUFNLS0jCZTMyfP59HH32U5cuXk5qaypw5cwgICGDWrFkAWK1WbrvtNu655x7Wrl3L9u3bufnmmxkwYABXXnklAH379mXKlCnMnTuXpKQkkpKSmDt3LtOmTaN3794AJCQk0K9fP2bPns327dtZu3Yt9957L3PnznXNPs2aNQuLxcKcOXNITU1l+fLlPProoyxYsOC8yxdFRKRt2J5Ws7JiSFx7zxYizYrZy8S/fjoQH7OJL/accP1/IiKekVlYTtKRPACmD+ro4WrOr8Eha9u2bQwZMoQhQ4YAsGDBAoYMGcJf//pXAO677z7mz5/PHXfcwbBhwzh+/DirV68mKOj7DiBPPvkkM2bMYObMmYwdO5aAgAA++eQTzGaza8zSpUsZMGAACQkJJCQkMHDgQF5//XXXcbPZzIoVK/Dz82Ps2LHMnDmTGTNmsGjRItcYq9XKmjVryMjIYNiwYdxxxx0sWLCABQsWNPyZEhGRVml7WiEAl3QOOfdAaXN6RQZx9aCaT8xf+vqIh6sRadte+eYIhgGju3WgU0iAp8s5L5Oh3qTnVFRUhNVqxWaz6fwsEZFWpsRezYAHPscwYMufryAi2M/TJUkzsyeriKv+8zVeJlj/h4nEhjb/N3cirY2trIoxj62ltNLBK78Y7tGtNuqbDRqlu6CIiEhL8F16IYYBMe39FbDkjPp2DGZsjw44Dfj4u0xPlyPSJr2zLZ3SSgd9ooKY0KtlbLOhkCUiIm3WmlMbEA/p3N6zhUizNnVANPD9/y8i0rRW767pFj5rZOcW01dBIUtERNqk3ZlFvJ50DIAbhmnjeTm7K/rWLE1KSS8kp0jt3EWaUkFpJcnHahrPXNH3zFswNUcKWSIi0iY9unIPDqfB1AEdXRvPipxJZLCfa+PTtXtzPFuMSBuzbn8OTgP6RAUR04K22VDIEhGRNqfEXk3S4ZpWwH+Y3NvD1UhLkNCv5hP0z3dle7gSkbZl7Z6aDzaubEGzWKCQJSIibdCWI3lUOw06hwbQJSzQ0+VICzAlPgqAjQdOUlhW6eFqRNqGKoeT9ftzAbi8r+c6Cl4IhSwREWlzNh2smcUa072DhyuRlqJ7eDv6dgym2mloNkukiWw9mk9xRTUdAn0Z1Km9p8tpEIUsERFpc745dCpk9QjzcCXSkkwb2BGAT3dkebgSkbbhy1NLBSf2icDs1TK6CtZSyBIRkTbDMAze3prGnqwiAEZ300yW1F9tyPrm4EkyCso8XI1I6/flqUYzV/RpWUsFQSFLRETakGfXH+KP7+8EYHL/SMKDLB6uSFqSuA6Bro2JX9101NPliLRqy7dncPhkKT5mE+N6trxVBwpZIiLSJhzMKeHfaw4A8Psre/G/WZd4uCJpiW4b1xWAZVvSKbFXe7gakdYnr8TOY5/t5d53dwDwy7FdCfLz8XBVDaeQJSIirV61w8kf399BpcPJ+F7h3H1FD7zNegmUhpvQK4Ju4YEU26tZqXOzRNxu3lvbeW79IRxOg+uGxPDHKX08XdIF0SuMiIi0ev/98iDJxwoIsnjzyLXxmEwt6wRqaT68vExcMygGgNW7T3i4GpHWxTAMdh63AfCvnw5k0Q2D8GphDS9qKWSJiEirlnQ4j6e/rFkm+PC18XQKCfBwRdLSTTq1MfHGg7mUVzo8XI1I65FXWklxRTUmE0wfFN1iAxYoZImISCtWUFrJ/GUpOA24YWgnrhkc4+mSpBXo2zGImPb+VFQ5+fpArqfLEWk1jp4sBSDa6o+fj9nD1VwchSwREWmVDMPgvvd3kF1UQbfwQB68pr+nS5JWwmQyuWazPkrJ9HA1zZNhGOSV2MkvrfR0KdKCHD4VsrqFB3q4kovn7ekCRERE3Kmy2snja/ax9Ug+36YV4mv24r8/G0KAr17yxH1mDotlyaajrNiZxW8ybAzoZPV0Sc2Gw2kwe/FmNp3a9PvSnmH8fGQco7t3wOrf8rrESdM5cipkdQ1TyBIREWk2DMPgz8t38l5yhuuyP13Vh/gYvQEW9+oXHcyMwdF8mJLJIyt389bcUac1VNmXXczCz/ZgK69iSGwId0zsTli71rs32+pd2Tz91UGirf6ugAXw9YGTfH3gJFZ/H965fTS9o4I8WKU0Z7XLBbt0UMgSERFpNpZvP857yRmYvUz8aUofukcEMrF3hKfLklbqnoTerEzNJulwPq8lHuPWMV2Amk/jn1t3iOXbj1PpcAKwPa2QD7ZnMP+KnvSLtuJjNjGkc4gHq3cfW1kVH393nAc/2U2102BHRk13uL/PiGdCr3AWbzzCF3tOkFFQzm/eSOb120aoAY2ckWsmqxUsFzQZhmF4uojmrKioCKvVis1mIzg42NPliMg5GIbB8cJyth0t4FheGcO6hFBW6SDYz5sRXUPVtruVMwyDqf/dyO6sIn5/ZS9+d2VPT5ckbcAr3xzhwU924+vtxRMzB+Hnbeb3b6dQfGqj4iv6RHD14GieX3+Y3VlFda77v1mXMHVgR0+UfUYl9mq+2H2Cib0jsAbUb1nfzgwbP38piaKKmsc7smsoe7KKGN4llBdvGebqDpdfWsn0pzZyvLAckwn6RgVzZb9IfndFT8wtuIOcuI/TadD3r6uwVztZd+8EujTTJYP1zQaayRKRFs1e7WDtnhxW7sxi29ECsosqzjhufK9wrP4+pGbaCPT15leXdmXawGjXi7vTabToVrEC244VsDurCD8fL24dE+fpcqSNmDOmC98cPMkXe3K4683trsuHxoXwp6v6MCwuBJPJxE8GdOTtreks3niEwrJKCsqq+NMHO+jbMYhu4e08+Ahq7Mgo5M43vyU9v5xR3UJZdMMgUo/buLJv5Bk37t6RUcgXu0/w1tZ0iiqq6dIhgBuGxXL7Zd3wOvWB1g//poYG+vLKL4bzt492kXg4j91ZRezOKqK9vw+/HNe1yR6nNF/7c4qxVzvx9jLRKcTf0+VcNM1knYdmskSarxJ7NTc+n8iuzO8/Hfb2MtE/xkqnEH+2HsknyM+bY3llVDtP/1PXNSyQsHa+HDlZxskSOyEBPozrGc6vxnVlUGz7JnwkcrGqHU5ufWUL3xzM42fDY3ns+oGeLknakCqHkyfW7GfxxiME+Jq5Kr4jf5ve76wtqKscTmY+n8j2tEICfc08dE081w/t1MRVf6+4ooorn1jPiSK767IAXzNllQ4GdrJySecQLokLYUhse97ckkZBaSXvJmfgOPV3tU9UEO/+ZjRBfvWb/cosLOftren8Z+0B/H3MfPa7S5vtrIU0nTvf/JYVO7KY1C+SF28Z5ulyzqq+2UAh6zwUskSaJ3u1gzuXfssXe3JoH+DDTSM6c1nPcAbHtsfft+4bmz1ZRazcmUU7ize9IoPYlWnjpY1HKCyrOuNte5ngzz/py23jumqJYQtgGAb/76NU3khKw8/Hi0/njaNHhE6sl6ZX7XBi9jLV6+/GiaIK5r25nS1H8wH4zfju/HFKb4/8zXng410s2XSUuA4BjO8VzmuJx844zsdsosrx/dvGK/tGMKRzCD8bHkuHBjb0cDoNZr2URNLhfLqHB/L+b8fQPsD3oh6HtFypx21Me2ojJhOsvPtS+nZsvu+5FbLcRCFLpPnZk1XE799OYW92Mb7eXrz961ENPoG8xF7NqtRsfMwmuoW1IzLYQnpBOS9vPMKKnVkAXHdJDI9eO6DFb4jYmjmdBv/8fB/PrT+EyQTP/nwoU+KjPF2WSL04nAb/WXuA/649AMDPR3bm79fEN9nSZcOouf9/f1Fz/6/fNoKBndpz7f++Icjfh39cP4DPU09wssTOW1vSqHYaDOncnks6hzAsLoQp8VEXFQqzbRVc+8w3ZNlq9rL75/UDGdYl1F0PT1qIaoeT657dxI4MG9MHRfPUTUM8XdI5KWS5iUKWSPOydPMxHvh4F1UOgw6Bvjw+cxAT3Ng9zjAMXvnmKI+s3IPDaTCqWyivzBlx2uyYeN7Szcf4zxcHyCmuWeL08Ix4bh6lc7Gk5XlnWzp/fH8HhgHD4kJ46Jp4+kU37nuO44Xl/OHd71yt1u+c2J0/TO4D1PwdBOoEqL3ZRezNKmbawI5nPEfrQu3LLubmxZvJLbbjYzbx8V3jmvUshrjfc+sP8dhnewn282bNgvFEBvt5uqRzUshyE4UskeYj+Vg+NzyXiNOAK/tG8tj1Axptz5mNB07ymzeSKbFXM6JrKP/92RCirM37D39b8o9Ve3l23SEAgize/PGqPgpY0qJ9lHKcP72/k/IqBwG+Zl68ZRhje4Q1yn2VVzqY9tTXHMotxc/Hiwev7s+Nwzs3yn3Vh62sirve+pavD5xkUGx77pnUi0viQmhnUX+21s5e7WDUo2spKKvin9cPZObwWE+XdF71zQbu+yhCRKQRHTlZyvy3U3AacN2QGF68ZWijbuo5rmcYr/5yOIG+ZrYcyWfyvzew/0Rxo92f1N+uTJsrYN2b0Ivk/zdJAUtavGsGx/DlveMZ26MDZZUOfvHKVradOl/LnZxOg4c+3c2h3FIigiys+t1lHg1YANYAH/7100G0s3jzXXoht7y8hclPbuBgTolH65LG9/muExSUVREV7Md1l8R4uhy3UsgSkWbvq305/OQ/X5OeX06nEH8euKZ/k5wcPjQulI/uGkv/6GBs5VX84b0drm5a4jmvbjoKwNSBHbnr8p74euulTFqHjlZ/Xp4znCv7RlDpcPLr15O5881v+SjluFtu31ZWxc9eSOKtLWkALLphULPp6hdl9eORa+PpGhZIh0BfjheW87MXEiksq/R0adKIlp36f3Hm8Fi3LkNtDlrXoxGRVudYXil3v7Wd8ioHY7p34J3bRxNczzbB7tAjIoiX5wwn6NQnrP/6fJ+ClodUVjvZejSfj1IyAfjl2C6eLUikEVi8zfz3piHExwSTX1rJih1Z/G5ZCv/34U4KSi8ucLzw9SG2HM0nwNfMY9cN4LJe4W6q2j2uGRzDV/dOYPXvL6N7eCAnSyp55tSstbQ+mYXlbDqUh8kEM4d5bguDxqLFriLSbFU5nNz15naKK6oZGhfCkl+M8MisRWSwH3+e2pf7P9jJc+sPsSvTxtM3XYI1oOnCXltWXung7mXb+XJvjivgDoip2btHpDUK8PVm6a9G8emOTA7llPLyN0d4IymND749zk0jOjP30m5EWf3IKarg4RV7CA+y0CuyHcUV1XQLDyTQ15tj+WXkFtuZEh9F9/B2VDmcvLMtA4B//XQQUwd29PCjPLsO7Sz837R+/OKVrSzZdJRZIzo3mxk3cZ+1e3OAmmYvnUICPFyN+ylkiUiz9fz6Q+w8bqN9gA//m3WJR5eF/Wx4LGYvE3/7aBdfHzjJ9Kc3cnmfCHy9vUjLK6PS4eSha/q3yhcKT6qsdvK7ZdtZs/sEAFZ/Hy7rFc59kz2zn5BIU7H6+/DzkTXnGl7aK4xFn+9jV2YRizce4bXEo1wV35HdWUXnPW/pX5/vY3yvcHpGtCO32E5YOwsJ/SOb4iFclAm9whndrQOJh/P42QtJvPKL4eo62Mp8uafm7/rlfZr//48Xwu3vWB544AFMJlOdr6io7/csMQyDBx54gOjoaPz9/ZkwYQK7du2qcxt2u5158+YRFhZGYGAgV199NRkZGXXGFBQUMHv2bKxWK1arldmzZ1NYWFhnTFpaGtOnTycwMJCwsDDuvvtuKiu1tlekuSsoreQvy3fy5Km9Wx6Y3t/jnf1MJhMzh8Xy/m/H0NHqR1p+GUs2HeWFDYdZtSubL/fmcOPzScxftp0XNxzmRFEFy7akkW2r8GjdLVnioTwm/3sDq3efwNfsxZu/GknKXyfx1E1DiA1VmJW2Y2LvCD6dN44lvxjOiK6hVDkMPv4uk4M5JUQF+3HtkBgu6xXOTwZE0ScqiG5hgYzoEsplvcIxmWD9/lxe2ngEqFmW5dMCzn0xmUw8eeNgeka0I7uogmv+9w0vbzyCmmK3DmWV1XxzavuAK/u6bxuW5sTtLdwfeOAB3nvvPb744gvXZWazmfDwmnW///jHP3jkkUdYsmQJvXr14uGHH2bDhg3s27ePoKAgAH7729/yySefsGTJEjp06MA999xDfn4+ycnJmM01e9VcddVVZGRk8MILLwDw61//mi5duvDJJ58A4HA4GDx4MOHh4Tz++OPk5eVx6623ct111/HUU0/V+/GohbtI09qeVsCdS78l81Q4uXFYLI9dP6BZzVoUllWyevcJDuWU4DQMwoMsLNuSzuGTpa4xJhMYBgT6mlmQ0JtbR8e1upN6G0u1w8nzGw7z+Op9OA0Ia+fLY9cN5Mp+rfPTTpGG2p5WwOrdJ0jPL2PBpF50C2931rHH8kpZujmNQzklBFq8eeDq/oQG+jZhtRensKyS+W+nsG5fLgAzBkfz55/0JaKZ76UkZ/fh9uP898sDHM4tJTbUnw1/mNisXuPPx2P7ZD3wwAN8+OGHpKSknHbMMAyio6OZP38+f/zjH4GaWavIyEj+8Y9/cPvtt2Oz2QgPD+f111/nxhtvBCAzM5PY2FhWrlzJ5MmT2bNnD/369SMpKYmRI0cCkJSUxOjRo9m7dy+9e/fms88+Y9q0aaSnpxMdHQ3AsmXLmDNnDjk5OfUOTApZIk0nLa+MaU99TVFFNd3CAll43QBGduvg6bLq5WSJnbe3plNe6eDVTUcptlfTPsCHwrIqoOYcohdvGebxGbnmLLOwnDeSjvHFnhPsP1GzBOr6SzrxwNX9CGrCZici0rwYhsGSTUd5eEXNJvHeXiZmj47jj1P64OejjeJbkq1H8/nZC0mu82t/d0VPfj+pl4erapj6ZoNGOSfrwIEDREdHY7FYGDlyJI8++ijdunXjyJEjZGdnk5CQ4BprsVgYP348mzZt4vbbbyc5OZmqqqo6Y6Kjo4mPj2fTpk1MnjyZxMRErFarK2ABjBo1CqvVyqZNm+jduzeJiYnEx8e7AhbA5MmTsdvtJCcnM3HixDPWbrfbsdvtru+Liorc+dSIyBnkFFfw0tdHWJWaTVFFNYNj2/PGr0a2qI0ow9pZuHNiDwBmj47jcG4pI7qG8s62dBau3MPO4zZ+9kIif7u6P6O7ddAbgx/Zk1XELS9vIbe45u+v1d+Hv0ztyw1DO7WoTzhFxP1MJhO/GNuVfh2D+dfn+9h2rIBXvjnKqtRsuoYFsieriBuGxfKnKX3w8tLfi+bKXu3gd29tx+E0mDawI7+f1IturbihidvfwYwcOZLXXnuNXr16ceLECR5++GHGjBnDrl27yM7OBiAysu6Sj8jISI4dOwZAdnY2vr6+hISEnDam9vrZ2dlERJy+fjMiIqLOmB/fT0hICL6+vq4xZ7Jw4UIefPDBBj5qEblQe7OL+NWr28goKAdqloY9e/MlLSpg/VhksB+Rp5ay3DSiM+N6hHHTi0kczSvjF69spXNoAM/dPJR+0W17dtxe7WDtnhy+PVbAa0nHqKx20jsyiF+M7cKkfpF0aMTNpkWk5RnZrQPv/XYMX+3N4d53vyPLVkHWqaXlL2w4TG6xnX/+dGCLOOesLdp2tIBMW0XNEvDrB7bo1/n6cPuju+qqq1z/HjBgAKNHj6Z79+68+uqrjBo1CuC0TyUNwzjvJ5U/HnOm8Rcy5sfuv/9+FixY4Pq+qKiI2NjYc9YmIg33zcGTLN54hC9PtXDt0iGAOyb0YGKfCMKDWteb69jQAN79zWj+u/Yga3afIC2/jGuf+YZ7E3rzy3FdMbfBT14rqhzcsngLW47muy67tGeYWuOLyHlN7BPB13+cyOYj+WQWllNV7eTvK/awfPtxisqr+N/PL9FqgWZo/f6a8+om9I5o9QELmqCFe2BgIAMGDODAgQPMmDEDqJll6tjx+/0ZcnJyXLNOUVFRVFZWUlBQUGc2KycnhzFjxrjGnDhx4rT7ys3NrXM7mzdvrnO8oKCAqqqq02a4fshisWCxtK43eCLNzSvfHOHBT3YDNQ0iJvWN5NHrBhDWimcuOlr9WXjdAP44pbfrJO5HVu7hRFEF/zetn6fLa1IOp8Hdb21ny9F8gizeTOofyZT+UUzqF6mlgSJSLwG+3kzs/f2qptjQAO5Y+i1r9+Zwy+ItvDRnWJNuXC/nt+FUyGpum2A3lkafT7Xb7ezZs4eOHTvStWtXoqKiWLNmjet4ZWUl69evdwWooUOH4uPjU2dMVlYWqamprjGjR4/GZrOxZcsW15jNmzdjs9nqjElNTSUrK8s1ZvXq1VgsFoYOHdqoj1lEzm7/iWIWfrYXqGkl/OU9E3jhlmGtOmD9UPsAX16ZM5y/X9MfgMXfHCHxVBvblqCiykHqcRupx20kH8vnrS1pfJRynBNF529VbxgGe7OL+OP7O2rasnt78eKtw3hi5mAS+kcpYInIBbuibySv/XIEQRZvthzN52fPJ7nO8RTPO1FUwd7sYkwmuLRHmKfLaRJu7y547733Mn36dDp37kxOTg4PP/ww69evZ+fOncTFxfGPf/yDhQsX8sorr9CzZ08effRR1q1bd1oL908//ZQlS5YQGhrKvffeS15e3mkt3DMzM3n++eeBmhbucXFxp7Vwj4yM5F//+hf5+fnMmTOHGTNmqIW7iIdUVju59plv2JVZxMTe4bw8Z3ibfmN933vf8c62DEwmGNW1A7eMjiM2NIBu4YEE+DaPpRS2sipuejGJAznFVDsNzvaKEdPeny8WjMff9/slOoZhkHQ4n9W7s/Exe7HlSD4p6YUAeJngmZ9fwpT4jme+QRGRC7Ar08atL2/hZEkl3cICefv20a1uCXpL9NaWNO7/YCeDYtvz0Z1jPV3ORfFYd8GMjAxuuukmTp48SXh4OKNGjSIpKYm4uJpdy++77z7Ky8u54447KCgoYOTIkaxevdoVsACefPJJvL29mTlzJuXl5VxxxRUsWbLEFbAAli5dyt133+3qQnj11Vfz9NNPu46bzWZWrFjBHXfcwdixY/H392fWrFksWrTI3Q9ZROrp6S8PsCuziPYBPvzj+oFtOmAB/N+0fuQU21m3L5fEw3kkHq6Z0QqyeDN1YEd6RwVRUFpJSKAvXToE4u9rxt/HjJ+PmbLKaoorqhneJRR/XzOGYZBlq6C8ykGnEH8s3u45H+HRlXvYnVW3y2pIgI/rxPK+HYNJPW7jeGE5f/0olZgQf8oqHVj9ffhqbw7bjhXUua6fjxcDY9ozZ2wXBSwRcbv+0Vbe/c0Ybn5pM4dPljJ78WaW/GKEts/wsHe3pQMwuX/b2e/Q7TNZrY1mskQunmEYPLv+EP/6fB+GAf+bdQlTB+oNdq2MgjLeSErj813Z2MqryC+trPd121m86dcxmPSCMleXrQBfM1cPiubBa/o3OGzZyqtIzy8js7CcxMN5vPLNUQBe+cVw+kcHYzGbCfb3rhOQV6Vm85s3ks94e/4+ZqYP6kiArzeBFjO3julCRJDe7IhI4zp6spQbnk8kt9hO+wAfnvn5JYzp3jaWqTU3+08Uk/DkBsxeJhLvv7zFvwZ4bDPi1kYhS+TiVFQ5+PMHO/lg+3EAfjWua5tr9NAQTqfBN4dOsvHASY7mldKhnYWcIjtZtnIqqhxUVDmpqHLgY/bCZMIVrAC8vUz4mL0or3IAcFV8FE/dNATverQzNgyDp788yH/WHqDaWfdlYc6YLjxwdf9zXvfed3fw9YFcxnTvQHiQhfzSKtpZzPxmQnc6Wv0v8NkQEblwR0+WMu+t7ew8biPIz5uP7hxLt/B2ni6rzXnok928/M0RJvWL5MVbhnm6nIumkOUmClkiF84wDOa+to0v9uRg9jLxwNX9mT0qztNltRpOp0Fqpo2jeWUE+XkzulsHfM1erN2bw51Lv6XS4eT28d24/6q+57ydjQdO8p+1+9l6tGZpX1g7X2La+9M1LJBpA6O5vE+ENvgUkRbJXu1g1oubST5WQEz7mi6vbaW7XXOQbatgwqKvqKhy8sovhtfpCNlSKWS5iUKWyIV7LfEof/1oF77eXrx863DG9dRSjabyyXeZzHtrOwD/vnEwM4bEnHXc3cu2Yxjga/bioWv687MRnZuyVBGRRpVTXMENzyVyLK8MgHmX9+D3V/bSh0dNoLbB07C4EN79zehWcS52fbOBtsQWkUaxbEsaf/+0Zi+sP1/VRwGriU0fFM2cMV0AmP92CncsTcZWVlVnzLp9OSx4JwXDgGuHxLD+vgkKWCLS6kQE+bHy7ku5dXTNSoqnvjzIHUu/payy2sOVtW7Jx/J5NzkDgD9P7dsqAlZDaCbrPDSTJdJwbyQd4/8+TAXgmsHR/PvGwW3uj2tzUOVw8vjq/bz49WEcToPIYAuX94lkcKyVkyWV/OeLA1Q6nEwd2JH//mwIZn2qKyKt3PvJGdz/wU4qHU46hwZwx4TuXHdJJ3y9Ne/gThVVDn7yn685fLKUnw7txKIbBnm6JLfRckE3UcgSaZjv0gu54blEKh1O7pzYnXsTeitgedjODBt3vfWta6nMD00d0JF//2ywqyW7iEhrl3ysgN++kUzOqc2KO1r9uGV0F342PJaQQF8PV9c6PLpyDy9sOExksIXV88djDfDxdEluo5DlJgpZzVepvZptxwo4nFtC9/B2jOgaip+Pe/YGkgtzOLeEmc8ncbLEzuT+kTx381AFrGaivNLB+v05pKTb2JFRiJ+PmSv6RnDjsNh6dR8UEWlNyiqreXNzGi9sOOwKW6GBvjx+wyAm9mn5zRk8KfFQHj9/KQmnAS/PGcblfVrX3lgKWW6ikNV85BbbWbLpCJ+lZmM2mUjLL8Ne7XQd9/PxYmTXDlzWK5zxvcLpHh6oN/hNaEdGIb9+LZnsogr6dgzm7dtHEezXej65EhGR1qeiysHH32Xy4obDHMgpAWBK/yj+dFUfuoQFeri6luXrA7m8tSWNz1KzMQy47pIYnpg52NNluZ1ClpsoZDUPqcdt3PbqVk4U2etc3inEn96RQaRm2k471icqiD9M7s3lfSLqHbYqqhwczCmhvMpBr4igVjW93Zg27M/lV69to7LaSY+Idiz79SjC2lk8XZaIiEi9VFQ5eOyzvbyWeBSnAT5mE7+6tBvzr+zZ4E3d26K1e05w26vbXN9PG9iRR68b0Co/bFXIchOFLM/bciSfOa9soazSQffwQH4/qRdWfx/C2lnoExWEyWTCMAz2nyhh/f4cNuw/yZaj+VSemuUaGhfCZT3D6Wj1o2N7P4L8fNh53Eagr5kgPx9W78rmy705lFU6qHY6qXLU/Eq0s3jz85Gd6R0VxIAYK93D26nd6xnszS7ip88mUmKvZmLvcP5z05BW+UdVRERav33ZxTyycg8b9ucCEB8TzNLbRulD1/O4bclW1u7N4fI+Edyb0Jt+0a33PbNClpsoZHnWt2kF3PzSZsoqHYzp3oHnZg+t1xt4W1kVz204xCvfHKGiynne8T8UEuCDr7fXaTNjsaH+9O9oJS2/jN5RQQzrEkKP8HY4DXAaBpHBfsR1CGhTDQRyiiqY8b9vyLRVMKJrKK/fNkKf+ImISIu3KjWb+z/YQUFZFZf3ieClW4bpg9azyC+tZMQjX1DtNFjz+8voGRnk6ZIalUKWmyhkeU5aXhnXPvMNeaWVXNozjBdvGdbgxhYniip4LzmDjIIyMgsryLZVkFdaSZ+oIIoqqrCVVzGhVziT+0cRE+KP2ctETHt/DANWpmaxYX8uR0+WseN4Yb3Cmo/ZRK/ImmWKE1rBrubn8l16IX947zv2nyihW3ggH/x2DO0D1JVJRERah12ZNq57ZhP2aiczh3Xi4RkD1Or9DF755ggPfrKb/tHBrLj7Uk+X0+gUstxEIcszSu3VzPjfNxzIKSE+Jpi3fz2aQIu3x+opr3TwWWoWJ4rsdA0LYHdmEVuPFnCiqAIvLxMm4HhhOWWVDtd1xnTvwOhuHfj5qDhCW1FL2PzSSv71+V6WbU3HMCCsnS/v/3YMcR10grCIiLQuH3ybwT3vfodhQOfQAK4ZHM24HmEM7xLa5me2DMPgtcRj/P3T3VQ7Df5val9+dWk3T5fV6BSy3EQhq+kZhsGdb37Lyp3ZRARZ+GTeOCKD/Txd1nk5nQbHC8t56evDvJp4zHW5n48XNw6L5cp+kfTrGEyHFtoQorLaydtb03h8zX4Ky6qAms5Bf7qqDxFBzf/nIyIiciG+2pfD/GUp2MqrXJd1Dw/k9vHdmTE4ps3NbtmrHXy9/yTLU46zYkcWULPn4hM3DmoTpwwoZLmJQlbTe279IR77bC8+ZhPLfj2aoXEhni6pwfafKGbr0XyWbUln53Gb63Kzl4lR3UKJCvana1gAcR0Csfr7YPH2IiW9kCqHk/YBvhzMKcHf10xEkIVOIQEMiwuhxF5NsL8PVv+mPfm2strJu8npPPPVIY4XlgM1nRsfuiaeEV1Dm7QWERERTyi1V7N6dzZf7c3lq305FFdUAzXncY/pHsYvx3VhaFzbeE28/4MdvLUlHah5X3P/VX24bVzXNrNtjkKWmyhkNa3kYwXc8NwmnAY8PCOem0fFebqki2IYBomH8li6OY092UUczi29qNvz9jIxvEsoXcMDGdcjjMv7RLjOU7NXOzBhcn2iZhgGJ0sqKaqowt/HTHFFNeFBlvMuXXQ4DfZlF5NdVM6u40W8tSWNTFsFABFBFu6c2IOfj+ysDWxFRKRNKq6o4q0tabz09RHXRsYA1w6J4YGr+zf5h6FNqcRezfCHv6C8ysH0QdHMGdOlRX4YfjEUstxEIavpGIbB9c9u4tu0QmYMjubJGwe3uk9FDuWWsPlwPgVllRw4UUymrYKi8ipK7NX0iQom2M+b/LJKeka0o8phkFtsZ292EYdyS/Exm1zt5WsF+pq5JC6EgrJK9mYVU+00CGvnS/sAX7JtFZTYq0+rIdrq51qy2C08kG5h7cgsLCfTVjNLtS+7uM6LBtSEqzsmdOdnIzo3uPmIiIhIa1TlcLIjo5C3t6bzXnIGTgOigv24b0pvrh0S0+rewwC8sy2d+97bQbfwQNYuGN8qH+P51DcbeK6TgMiPfJaazbdphfj7mPnzT/q2yl/c7uHt6B7ersHXK6qoItDXm6N5pSQfK2B/djGfpWZzvLCcrw+crDP2ZEklJ0sqATCZoJ2vN+VVDoL8vCkoqyLTVuGamfrhUsYfCrJ40yk0gB4R7RjdrQPXXRKjcCUiIvIDPmYvhsaFMjQulBuHd2bBOykcyytjwTvfUVBWxW3junq6RLcpLKtkxc4sFn99BICfDu3UKt+nuZNmss5DM1lNI6/EzpT/fE1usZ27L+/BgoTeni6p2TMMg2/TCjmYU0z7AF/6dQwmwNdMdlEFBaVVRFlrzuf6YTgqLKvk8MlSCkorcRqQkl7AyeJKYkL8iW7vj5cJQgJ8GdsjrM2dyCsiInIxKqocPPnFfp5ff5hgP2823DexVWxt8sG3Gfzpg51UVtdsZeNr9mL9fRPoaPX3cGWeoeWCbqKQ1fiqHE5ufz2ZL/fm0COiHZ/OG6dZExEREWlxHE6Dqf/9mr3ZxYQE+NC3YzB/mdqX/tFWT5d2QU4UVXDF4+spsVfTt2MwCf0iuaJvBAM7tfd0aR5T32ygj6rFoyqqHPz2jZq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      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['ninfav'] = df['ninfected'].rolling(window=7).mean()\n",
    "df['ninfav'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to be able to compare several countries, we might want to take the country's population into account, and compare the percentage of infected individuals with respect to country's population. In order to get country's population, let's load the dataset of countries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UID</th>\n",
       "      <th>iso2</th>\n",
       "      <th>iso3</th>\n",
       "      <th>code3</th>\n",
       "      <th>FIPS</th>\n",
       "      <th>Admin2</th>\n",
       "      <th>Province_State</th>\n",
       "      <th>Country_Region</th>\n",
       "      <th>Lat</th>\n",
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       "      <th>Population</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>AF</td>\n",
       "      <td>AFG</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Afghanistan</td>\n",
       "      <td>33.939110</td>\n",
       "      <td>67.709953</td>\n",
       "      <td>Afghanistan</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "      <td>41.153300</td>\n",
       "      <td>20.168300</td>\n",
       "      <td>Albania</td>\n",
       "      <td>2877800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>DZ</td>\n",
       "      <td>DZA</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>28.033900</td>\n",
       "      <td>1.659600</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>43851043.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>AD</td>\n",
       "      <td>AND</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>42.506300</td>\n",
       "      <td>1.521800</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>77265.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24</td>\n",
       "      <td>AO</td>\n",
       "      <td>AGO</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>-11.202700</td>\n",
       "      <td>17.873900</td>\n",
       "      <td>Angola</td>\n",
       "      <td>32866268.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4191</th>\n",
       "      <td>84056037</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>840.0</td>\n",
       "      <td>56037.0</td>\n",
       "      <td>Sweetwater</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>US</td>\n",
       "      <td>41.659439</td>\n",
       "      <td>-108.882788</td>\n",
       "      <td>Sweetwater, Wyoming, US</td>\n",
       "      <td>42343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4192</th>\n",
       "      <td>84056039</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>840.0</td>\n",
       "      <td>56039.0</td>\n",
       "      <td>Teton</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>US</td>\n",
       "      <td>43.935225</td>\n",
       "      <td>-110.589080</td>\n",
       "      <td>Teton, Wyoming, US</td>\n",
       "      <td>23464.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4193</th>\n",
       "      <td>84056041</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>840.0</td>\n",
       "      <td>56041.0</td>\n",
       "      <td>Uinta</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>US</td>\n",
       "      <td>41.287818</td>\n",
       "      <td>-110.547578</td>\n",
       "      <td>Uinta, Wyoming, US</td>\n",
       "      <td>20226.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4194</th>\n",
       "      <td>84056043</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>840.0</td>\n",
       "      <td>56043.0</td>\n",
       "      <td>Washakie</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>US</td>\n",
       "      <td>43.904516</td>\n",
       "      <td>-107.680187</td>\n",
       "      <td>Washakie, Wyoming, US</td>\n",
       "      <td>7805.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4195</th>\n",
       "      <td>84056045</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>840.0</td>\n",
       "      <td>56045.0</td>\n",
       "      <td>Weston</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>US</td>\n",
       "      <td>43.839612</td>\n",
       "      <td>-104.567488</td>\n",
       "      <td>Weston, Wyoming, US</td>\n",
       "      <td>6927.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4196 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           UID iso2 iso3  code3     FIPS      Admin2 Province_State  \\\n",
       "0            4   AF  AFG    4.0      NaN         NaN            NaN   \n",
       "1            8   AL  ALB    8.0      NaN         NaN            NaN   \n",
       "2           12   DZ  DZA   12.0      NaN         NaN            NaN   \n",
       "3           20   AD  AND   20.0      NaN         NaN            NaN   \n",
       "4           24   AO  AGO   24.0      NaN         NaN            NaN   \n",
       "...        ...  ...  ...    ...      ...         ...            ...   \n",
       "4191  84056037   US  USA  840.0  56037.0  Sweetwater        Wyoming   \n",
       "4192  84056039   US  USA  840.0  56039.0       Teton        Wyoming   \n",
       "4193  84056041   US  USA  840.0  56041.0       Uinta        Wyoming   \n",
       "4194  84056043   US  USA  840.0  56043.0    Washakie        Wyoming   \n",
       "4195  84056045   US  USA  840.0  56045.0      Weston        Wyoming   \n",
       "\n",
       "     Country_Region        Lat       Long_             Combined_Key  \\\n",
       "0       Afghanistan  33.939110   67.709953              Afghanistan   \n",
       "1           Albania  41.153300   20.168300                  Albania   \n",
       "2           Algeria  28.033900    1.659600                  Algeria   \n",
       "3           Andorra  42.506300    1.521800                  Andorra   \n",
       "4            Angola -11.202700   17.873900                   Angola   \n",
       "...             ...        ...         ...                      ...   \n",
       "4191             US  41.659439 -108.882788  Sweetwater, Wyoming, US   \n",
       "4192             US  43.935225 -110.589080       Teton, Wyoming, US   \n",
       "4193             US  41.287818 -110.547578       Uinta, Wyoming, US   \n",
       "4194             US  43.904516 -107.680187    Washakie, Wyoming, US   \n",
       "4195             US  43.839612 -104.567488      Weston, Wyoming, US   \n",
       "\n",
       "      Population  \n",
       "0     38928341.0  \n",
       "1      2877800.0  \n",
       "2     43851043.0  \n",
       "3        77265.0  \n",
       "4     32866268.0  \n",
       "...          ...  \n",
       "4191     42343.0  \n",
       "4192     23464.0  \n",
       "4193     20226.0  \n",
       "4194      7805.0  \n",
       "4195      6927.0  \n",
       "\n",
       "[4196 rows x 12 columns]"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries = pd.read_csv(countries_dataset_url)\n",
    "countries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because this dataset contains information on both countries and provinces, to get the population of the whole country we need to be a little bit clever: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UID</th>\n",
       "      <th>iso2</th>\n",
       "      <th>iso3</th>\n",
       "      <th>code3</th>\n",
       "      <th>FIPS</th>\n",
       "      <th>Admin2</th>\n",
       "      <th>Province_State</th>\n",
       "      <th>Country_Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long_</th>\n",
       "      <th>Combined_Key</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>790</th>\n",
       "      <td>840</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>840.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "      <td>40.0</td>\n",
       "      <td>-100.0</td>\n",
       "      <td>US</td>\n",
       "      <td>329466283.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     UID iso2 iso3  code3  FIPS Admin2 Province_State Country_Region   Lat  \\\n",
       "790  840   US  USA  840.0   NaN    NaN            NaN             US  40.0   \n",
       "\n",
       "     Long_ Combined_Key   Population  \n",
       "790 -100.0           US  329466283.0  "
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries[(countries['Country_Region']=='US') & countries['Province_State'].isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pop = countries[(countries['Country_Region']=='US') & countries['Province_State'].isna()]['Population'].iloc[0]\n",
    "df['pinfected'] = df['infected']*100 / pop\n",
    "df['pinfected'].plot(figsize=(10,3))\n",
    "plt.show()"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "\n",
    "## Computing $R_t$\n",
    "\n",
    "To see how infectious is the disease, we look at the **basic reproduction number** $R_0$, which indicated the number of people that an infected person would further infect. When $R_0$ is more than 1, the epidemic is likely to spread.\n",
    "\n",
    "$R_0$ is a property of the disease itself, and does not take into account some protective measures that people may take to slow down the pandemic. During the pandemic progression, we can estimate the reproduction number $R_t$ at any given time $t$. It has been shown that this number can be roughly estimated by taking a window of 8 days, and computing $$R_t=\\frac{I_{t-7}+I_{t-6}+I_{t-5}+I_{t-4}}{I_{t-3}+I_{t-2}+I_{t-1}+I_t}$$\n",
    "where $I_t$ is the number of newly infected individuals on day $t$.\n",
    "\n",
    "Let's compute $R_t$ for our pandemic data. To do this, we will take a rolling window of 8 `ninfected` values, and apply the function to compute the ratio above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Rt'] = df['ninfected'].rolling(8).apply(lambda x: x[4:].sum()/x[:4].sum())\n",
    "df['Rt'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that there are some gaps in the graph. Those can be caused by either `NaN`, if  `inf` values being present in the dataset. `inf` may be caused by division by 0, and `NaN` can indicate missing data, or no data available to compute the result (like in the very beginning of our frame, where rolling window of width 8 is not yet available). To make the graph nicer, we need to fill those values using `replace` and `fillna` function.\n",
    "\n",
    "Let's further look at the beginning of the pandemic. We will also limit the y-axis values to show only values below 6, in order to see better, and draw horizontal line at 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CdZ3eiM/mWqIRUXIjPuCXabEy0zISYtCXHK+DThP8n9qbzxmDlHhPtuWDr6tDtT0iopjDoEXhrHaWhxGFktSIH9VdhI+YaREPRqTgNHUE38/iL9Gow10LxgMA/rbuCGxOZsuJaHRi0KJw3X3Kwxi0EI2EkhvxASDNxExLKARzsORAbphXhKwkA6rburFy26kR3x8RUSxi0KJwYg+LTqPyvs+ghWgkfD0t0d1HuIhN483saRkRMVOVkWgc8X0ZdRrc+40SAMDfPz3KM3SIaFRi0KJw3Q5P0CKeE8DyMKKR8Z3TosyoJc3ERvxQECeHhSLTAgDfnlWAwtR4NHXa8MrmEyG5TyKiWMKgReHEzIr46imnhxGNjNSIH+V9hEsaRx6HxEjOaOmPXqvGAxd5si3LNx6HpdsRkvslIooVDFoUTpweJpYosDyMaGSUXh4mXmS3dNl5oOEISOVhIQpaAODqM/JQkpkAS7cDL35+PGT3S0QUCxi0KJxY+yxmWlgeRjQy4mW8UhvxU+J1UKk8wVkLm/GDJmZagj1Ysj8atQo/XjgRAPDSpgqW8BHRqMKgReHETEumVB7GoIVoJASFn9Oi1aiREi9OEONFcbBCXR4mumRqFsryzeiyu/DM+mMhvW8iIjlj0KJwvvIwb9Bid8HNkg+ioEnlYYrtavE1jzd1MNMSDLdbkHqC0hND04gvUqlU+Ik32/LG1pOoaesO6f0TEckVgxaFEzMrmX4lCl0ONuMTBcut8EwL4JsgxkxLcCzdDji9Lw6J38tQOrckHXOLU2F3uvH3T4+E/P6JiOSIQYvCiSOP0xL0UHsvslgiRhQ8XyO+cqOW9ERx7DEzLcEQD5Y0x+mg14b+z6xKpcJPL/FkW97dUYWKJmvIvwYRkdwwaFE4ccRxvF4Lk0ELgM34RCPha8SP6jbCKs3kLQ9jo3dQmjpC34R/ulljUnHBxAy43AKeWnc4bF+HiEguGLQoXLd3eli8XoMEb9DCTAtR8JR+Tgvg62lpZtASlEapCT+0/SynEyeJfbS7BkfqO8L6tYiIoo1Bi4K53YLUv8JMC1GIjIbyMO/EKx4wGRyxrC7Uk8NOV5pnxnkTMiAIwGdHmsL6tYiIoo1Bi4L1OF1S/X28XiMFLWLJGBEFTsy0KLo8LEHsaWGmJRjiwZLhDloAYGZhCgBgX40l7F+LiCiaRhS0LFu2DCqVCg888ECItkOhJI47BoA4nQYJBg0AlocRjYRvYLhyo5Y0ceQxMy1BCcfBkgOZmpsEANhX3R72r0VEFE1BBy3bt2/H888/j7KyslDuh0KoS2rC10CtVsGkZ3kY0UiJ2UslZ1oy/DIt4mGacueS0flTUtASgUxLaZ4ZAHC0sRM9HGdPRAoWVNDS2dmJJUuW4IUXXkBKSkqo90Qh0uXwNeEDYCM+UQiMinNavJkWm9MNq12+F8KnWrrwwmfHce0zX2D8z/+D//ufPM4sEYOWUB8s2Z+sJAPSE/RwuQUcrGMzPhEpV1BBy913343LL78cF1100ZBrbTYb2tvbe71RZPiPOwbg19PCoIUoWOLr+SoFl4fF67XSix1ymyB2rLET/1h/FFf8/XOc+8f1+P1/DuCryjYIArB6b120twcAaOqITCM+4BkIMSXXk23ZW82+FiJSLm2gN1i5ciV27tyJHTt2DGv9smXL8NhjjwW8MRq5bruvPAyA3/Qw+b5ySiR3YrmUWuFjTNIS9Ohq6UZTpw1Faaao7kUQBLzw+XG8v7Mah/xG+6pVwJziVMwpTsP//e8Ijjd2wuUWoIli7Z7bLfgyLREIWgCgNDcJnx1uxL4avihIRMoVUNBy6tQp3H///fjvf/8Lo9E4rNs88sgjePDBB6X329vbUVBQENguKShW++nlYWzEJxopscVDyZkWAEgzGXCqpVsWzfjl1Rb84T8HAQBatQpnj0/HotJsXDwlC+kJBrjcApZvPAab042q1q6oBlmWbgec3v6atDCf0yKa6s20cIIYESlZQEHLzp070dDQgJkzZ0ofc7lc+Oyzz/D000/DZrNBo9H0uo3BYIDBEJlXm6i3Lilo6V0e1mln0EIULGEU9LQAviyBHMYeH6nvBACU5Zvx+i1zYY7X9fq8Rq3C2IwEHKhtx9GGzqgGLeL3yxyng0GrGWJ1aJTmeSaIHaztgMPlhk6j8DQgEY1KAT2zXXjhhSgvL8euXbukt1mzZmHJkiXYtWtXn4CFoqtrgPIwZlqIguceBYdLAr7T3OVwwGRFkxWAZ1LW6QGLqCQzAQBwpKEzYvvqT6NUGhaZLAsAFKbGI9Gohd3lxtEoP34ionAJKNOSmJiI0tLSXh8zmUxIS0vr83GKPv+RxwCnhxGFgq8RX9nSpKAl+pkWMWgZmz5wBmW8N2iJ9kV7JA+WFKlUKkzJScLWihbsrbZgck5SxL42EVGkMIesYFKmxXBaeRgb8YmCJjXiKzxq8ZWHySfTUjxI0CKXTIv4/UqPwMGS/sTzWtiMT0RKFfD0sNNt2LAhBNugcBB7WkxsxCcKGWGUlIelyaSnRRAEKWgZM4xMy7GGTgiCELV/n0geLOlP7GthMz4RKRUzLQomZlrieE4LUcgI3gIxZYcsQLrJWx5mjW6mpb7dhm6HCxq1CgUp8QOuK0ozQatWodPmRF17TwR32FuTtzwsI8KZFnGC2P6adrjFxisiIgVh0KJg1tMyLSa9WB7GoIUoWKOmET9RHpmW402ecq+ClDjotQP/ydJr1ShK8wQ10exraYpCIz7g6fcx6tSw2l040WyN6NcmIooEBi0KNlAjvs3phtPljtq+iGKZrzwsuvsItzRvpqWtywFHFJ8vhtPPIirJTATgG5EcDY0RPlhSpNWopQb8vexrISIFYtCiYF0OMWjpXR4GAFY24xMFRSwPU3ojfkq8XnqMLVEsEatoFIOWhCHXShPEGqOYaenwNuJHOGgBgKm53r6Wava1EJHyMGhRsC6beLikJ9Oi16qh9x46xgMmiYIjZVoU3tWiVquQaop+iZiUackYRqYlyxu0RCnTIggCmq3R6WkBgNJcThAjIuVi0KJgp488BgATJ4gRjYg48ljp5WGAPA6YHM4ZLaJxGdHNtFi6HXC4PD8faRHuaQF8Y4/31likn1MiIqVg0KJgp488BvzPamHQQhSM0dKID/if1RKdTIvD5UZlSxeA4fW0jMtIgErlKWeLxqGY4sGSSUYtDFrNEKtDryQrAVq1Cm1dDlS3dUf86xMRhRODFgXzjTz2/fFM4NhjohEZLY34gC9bEK1MS1VrN5xuAUadGtlJxiHXx+k1yE+JAxCdCWJSE34USsMAwKDVYEKWZxgBS8SISGkYtCiYGLSIo44BntVCNFKjpREfANLEnhZrdDItJ8RDJdNMUA/zGy5NEItC0NLkDe4ifbCkPzbjE5FSMWhRKEEQpHNa4vstD+P0MKJguEdJIz4ApCd6Mi3iRKxIOx7AuGORNEEsGkFLR3QzLYB/XwszLUSkLAxaFMrmdEtlLP6N+AlsxCcamdHUiO/NtDRHKdNS4T1YMlaCFrE8LJqZltI8b6alhpkWIlIWBi0K5R+UxOn8Mi16NuITjYSYaVGPgqhFyrREqRE/kIMlRWLQcqShIyx7GoyUaYnC5DDRpOwkqFRAfbtNGgxARKQEDFoUSuxnMerU0PjVgrOnhWhkxJ6W0UDsaYlWI754sOTYYZzRIhKDlvp2G9p7HGHZ10DE4C4aB0uKTAatNB6a2RYiUhIGLQrVXxM+wOlhRCMljKJMi//0sEif+9Ftd6HG0gMAKE5PGPbtkow6ZCV5goZIl4hJjfhR7GkBfH0tnCBGRErCoEWhxDNa/McdA2zEJxop9ygaeSxmDOwuN9p7IvtCx4lmT5bFHKdDSrwuoNuKE8QiH7REP9MC+CaI7eUEMSJSEAYtCjVwpoWN+EQjIZaHjYKYBUadRsrORvqwRv9+lkAP8oxGM74gCL6gJdqZllxmWohIeRi0KFR/B0sCfj0tdgYtRMGQysNGw0Et8CsRs0a2r0UMWsYG0IQvikbQYul2wOHy/HCkmaLXiA8AU71BS2VLFyxdke3rISIKFwYtCiWWh5kMA5WHMWghCobY2zE6QhZfqVNThCdRHW8MfHKYKBoTxMQsS6JRC6NOM8Tq8DLH65CfEgcA2FfLEjEiUgYGLQpl9fasxOnYiE8USlI/+iiJWsSsQVOEMy1iT0txAJPDRCXeoKWqtRvd9sj07zV2yKMJXySWiO1niRgRKQSDFoUaKtNiZSM+UVBG0zktAJAWpUyLWB42Ji3woCUtwYCUeB0EATjWGJkSsUaZNOGLxEMm2YxPRErBoEWhxJ6W+NN6WsRGfJaHEQVnNDXiA0CG1NMSuaClrcuOFm9mJ5jyMMA3QSxSQYsY1GXIJGgR+1r2MtNCRArBoEWhfEFL7/Iw/8MlI33uApESjKZzWgBfpiWSB0yKWZasJIP0nBWocWJfS32EghYp0xLdJnzRVG+m5Xhjp5R5JyKKZQxaFEoqDxtgepjTLcDmdEd8X0SxTmrEHx0xi68RP4Ijj/3HHQerJMITxMTvj1x6WjITjchINMAtAAdqIzeQgIgoXBi0KJRv5PFpmRa/91kiRhS4UdaH7xt5HIVMS3F6QtD3EekJYk3e749celoAoNR7yOT+Gva1EFHsY9CiUAM14mvUKsTpeMAkUbDcUqZldIQtYrlTJDMtx0dwRouoJMsTtJxs7oI9Alnlxg55NeIDQGmet6+lmn0tRBT7GLQolG/kcd/zAnhWC1HwxJ6WURKzSBfh7T1O2JyRmTpYMYIzWkTZSUYkGLRwugWc9I5PDiepp0Um5WEAMNWbadnLTAsRKQCDFoUSzybor4lVnCDGscdEgRPLw0ZLI36SUQet2vNYWyJwVosgCCM6o0WkUqmkZvxw97UIgiCVz8mlER/wTRA7XN8RkWwTEVE4BRS0PPvssygrK0NSUhKSkpIwb948rF69Olx7oxGwesvD4vQDZ1pYHkYUOKkRP8r7iBS1WoVU8YDJjvAHLQ0dNnTZXdCoVShIiR/RfY3PEPtawhu0tHc7YXd5ggI5lYflp8QhOV4Hh0tAOc9rIaIYF1DQkp+fj8cffxw7duzAjh078I1vfANXX3019u3bF679UZCkTIu+b6aF5WFEwRtt5WGA3wSxCJzVctxbGlaQEge9dmTFAGJfS7gzLY2dPQCARKMWxn5KcqNFpVLhnHHpAICNhxujvBsiopEJ6C/ClVdeicsuuwwTJkzAhAkT8Pvf/x4JCQnYsmVLuPZHQRIzLacfLgkACcy0EAVttDXiA5GdICZODhszgn4WUaQyLY3eDJRcDpb0d/7EDADAxkMNUd4JEdHIBP0ylsvlwsqVK2G1WjFv3rxQ7olCwHe4JBvxiUJpNGZaMoZxVovT5Q5JRqOiyXMfI2nCF4mZluONnXC5w3eYru9gSRkGLRM8QcueaguaIzgBjogo1AIOWsrLy5GQkACDwYA77rgDH374IaZMmTLgepvNhvb29l5vFF6CIEhBCxvxiULLd07L6IlafJmW/i96BUHAHW/sxEVPbsSnB+tH9LUqQjDuWJSfEg+9Vg2b042q1q4R399A5HawpL+sJCMm5yRBEIDPjzRFeztEREELOGiZOHEidu3ahS1btuDOO+/EjTfeiP379w+4ftmyZTCbzdJbQUHBiDZMQ7M53dKriv024nv7XMQSMiIaPrE8TD16YhakSZmW/svD3t52CusOeMqPVu2uHdHXOh6CgyVFGrUK4zLC39fiO6NFPpPD/C3wlohtYIkYEcWwgIMWvV6P8ePHY9asWVi2bBmmT5+Ov/3tbwOuf+SRR2CxWKS3U6dOjWjDNDSxCR8A4nlOC1FojcLysPRBysMqm7vwu499L1x9dqQJ7iBLsZwuNyqbPRmRkYw79jc+M/x9LXIuDwOABd4SsZH82xARRduIz2kRBAE228B1sgaDQRqRLL5ReIkZFL1WDa2m7z8xG/GJgufLtIyeqGWgRnyXW8BP3tuNLrsLs8ekIF6vQVOnDQfqgisDrmrthtMtwKBVIyfJOOJ9A0CJGLTUDxy0bD3ejOUbj6EryOyzmIGS08GS/mYUpSDRoEWL1Y49HH1MRDEqoKDlZz/7GT7//HOcOHEC5eXl+PnPf44NGzZgyZIl4dofBcE37rj/0Zs8p4UoeKPxdep0U/+Zlpc3VWDbiRaY9Bo8+e0zcNbYNADAZ4eD652okErDTFCHqP5OzLQcbewbtDS09+D+lV/jO89vwbLVB/GT93ZL5/AEQu6ZFp1GjXPGe0cfH+LoYyKKTQEFLfX19bjhhhswceJEXHjhhdi6dSvWrFmDiy++OFz7oyBYpclhfZvwAcDkbcRneRhR4HzTw0ZPpiU90ZNpabHapfKiw/Ud+NN/DwEAfnHFFBSkxuO8Es+F8WdBngly3C9oCRUx03KsoVMKSJwuN17eVIEL/7IR/9pVA5UK0KpV+E95HZ7beDzgr9HUId9GfJHU13KYfS1EFJv6v6odwEsvvRSufVAIdQ1yRgvgXx7G6WFEgRqNjfipJk/Q4nQLaO9xwGTQ4sF3d8HudOOCiRn47mzPgJXzvL0TO062wGpz9ju9cDAnwhC0FKWZoFGr0Glzoq69BzVtPfjFP/fiQK2nhG16vhm/vaYUe6os+MU/9+JPnxxEaV4Szi3JGNb9C4LgKw+TaSM+4DuvZdepNrRa7UgxyXevRET9GXFPC8lPlzcYiR/ggoHlYUTB8408Hj0MWg0SjZ7njaZOG/7+6VHsrW5HcrwOT1xXJmWditNNyE+Jg8MlYMvx5oC/TigPlhTptWqMSYsHANz/9i5c9+xmHKhthzlOh99/sxQf3HUOyvKTsWRuIb49Kx9uAbj37a9xqmXoEckut4DfrjoAu8sNvUYt2/IwAMgxx2FSdiIEAfjsCEvEiCj2MGhRILERv7/JYYAv08LyMKIgeKOWUPVcxArxgMlPDzbgH+uPAgB+e3UpMv0a5lUqlZRtCaZELJRntPgT+1q2nWgBAHx7Vj4+/fH5WDK3CBrvv6NKpcJvri5FWb4ZbV0O3PHGTvQ4Bs5G9zhcuPvNr/DyFxUAgIcXTYJxgOdcuRCzLexrIaJYxKBFgaRGfAMb8YlCTSwPG10hi2+C2B/XHILLLeCKshxcOT23z7rzSnzjdQPR43Chuq0bQGjLwwBg9phUAMCk7ES8f+c8/HHxdOnsGX9GnQbPLp2JVJMe+2ra8bMPyvttzG/utOH6F7Zgzb466DVq/N/1Z+KW+cUh3XM4nC+NPm7k6GMiijkMWhRIbMSPG6IR32p38Q8XUYCka9hR1IgP+CZjOd0CMhIN+O3Vpf2uO3t8GjRqFSqarMMqsRKdaPZkWZKMWqmHJlRuOacYq+8/F6vunY+ZRamDrs1LjsPT3zsTahXwwdfVeO3Lk70+f7yxE9c+uxlfV7bBHKfD67fOwVX9BG9yNKsoFSa9Bk2dduyrCW4sNRFRtDBoUaBub3nYQCOPE/x6XboGKX8gor5GYyM+4Mu0AMAfrysbsJE7yajDzMIUAMDGAErEKhq9TfgZCSGfzKZWqzA5J6nfc6v6c/a4dDyyaDIA4Ler9mNbhaesbOfJFlz37GacbO5CQWoc3r/zbMz1jnmOBXqtb/TxhkOcIkZEsYVBiwL5Mi39By1xOo10wcUSMaLA+BrxR1fUMjXXDABYMrcQF0zKHHTteRMCH318PEz9LMG67dxiXDk9F063gLve/Aqvf3kC17+wFa1dDkzPN+ODO8+RemViyYKJnn+7DUGOpSYiipbA5lFSTPAdLtn/P69KpYJJr0WHzYlOmxNZkdwcUYwTy8NGW6blO7MKMKsoZVgX6udNyMCf/3sYm481w+FyQzeMDEdFGMYdj4RKpcIT103D4boOHKrvwC//tQ8AcNHkLPzf9WcMeA6W3InntXxd2Yq2LjuS4zn6mIhiAzMtCiRmT+IHaMQHgAQjm/GJgiE2Zo+ylhao1SqUZCUOq3SrNNeMVJMenTYnvq5sG9b9yy1oATwH9C6/YSaSvM+XN509BstvmBmzAQsA5CbHYUJWAtwCsOloYMMSiIiiiUGLAol9KgONPAZ8E8Q49pgoMKO1PCwQarUK88cHViIWjoMlQ2FMugkf33cu3vrBXDx65RRpRHIsE6eIbeDoYyKKIQxaFKhLyrQM/Gqgb+wxG/GJAuEepZmWQJ3nN153KP87UI9mqx06jUp2QQsAFKTG4+xx6SEfEBAtYl/LxsMcfUxEsYNBiwKJjfjxAzTiA0CCOPaYmRaigIg9LUq5gA2X80o8mZbyagtarPYB13XZnfiVt1/klvnF0gsqFD6zxqQgXq9BY4cN+2s5+piIYgODFgUaqhHf/3MsDyMKjK88jAaTmWTEpOxECALw+SDZlr/97wiq27qRlxyH+y8sieAORy+DVoOzx3mCykDGUhMRRRODFgWyes9pGWjkMeA7q4WZFqLAiI34aj57Dkk6gf1w/w3fB+va8dLnFQCAx66aGtMN7rFGnCK2kX0tRBQj+GdXgYaVaWHQQhQUqTyMuZYhiX0tnx9plII9kdst4Ocf7oXTLeCSqVm4aAqHr0eSGFDurGyFpdsR5d0QEQ2NQYsCDWfksW96GBvxiQLBRvzhmzUmBXE6DRo6bDhY19Hrc+/sOIWdJ1th0mvw6JVTo7TD0asgNR7jMkxwuQV8wdHHRBQDGLQoULeDjfhE4cJG/OEzaDU4a2wqgN6jj5s6bXh89UEAwI8unoDc5Lio7G+0E6eIbTjUEOWdEBENjUGLwtidbjhcnquqwerDpUyLnUELUSAEbys+Q5bh6W/08R8+PgBLtwNTcpJw09ljorQzEvta1h1oQK2lO8q7ISIaHIMWhenyC0IGy7Swp4UoOOKxFmpmWoZFDFq2V7Siy+7E5mNN+ODraqhUwO+/WQqthn+GomVOcSrGpMWjxWrHd5ZvQVVrV7S3REQ0IP61UJgubxO+XqOGbpCLAU4PIwqSVB4W3W3EirHpJuQlx8HucuOzw034xYd7AQBL5hbizMKUKO9udDNoNXjjtrkoTI1HZUsXvrN8CyqbGbgQkTwxaFGYrmGMOwbYiE8ULLE8TM2gZVhUKpWUbfn5h+U43mRFeoIBP71kUpR3RgCQnxKPd2+fh7HpJlS3dePby79ERZM12tsiIuqDQYvCdEnjjgcPWtiITxQctzS5l1HLcJ0/wXOQYbPVDgD45RWTYY7TRXNL5CfbbMTKH56FkswE1LX34NvLv8TRho6hb0hEFEEMWhTG6s2cxBsGP6SNPS1EwRE48jhgZ49Ph8abmjq3JB1XTc+N8o7odJlJRrz9w7MwKTsRjR02fGf5Fhysa4/2toiIJAxaFKbb4T2jZajyML1YHsaghSgQbMQPXJJRh2+emYesJAN+e3Upx0XLVHqCAW//4CxMzU1Cs9WO65/fgr3Vlmhvi4gIAIMWxZEyLUOWh3mCFpvTDafLHfZ9ESmFWB3Gy+7A/Plb07HlkQsxJt0U7a3QIFJMerx121mYXpCM1i4HvvfCFuw+1RbtbRERMWhRGrERf7AzWgBfeRjgC3SIaBi85WFqPnsGjBmW2GCO1+H1W+dgZlEK2nuc+N4LW7DR73BQIqJo4J9dhREb8YfKtOi1aui9I5F5wCTR8InlYSrmWkjBkow6vHrLHJwzPg1Wuwu3vrId/29nVbS3RUSjGIMWhRlu0AIAJk4QIwqYIB3UEt19EIVbgkGLFTfNwTVn5MLpFvCT93bjH+uPSsMoiIgiiUGLwgy3PAzwP6uFQQvRcLm9LWBsxKfRQK9V48lvn4Hbzx8LAPjTJ4fwi3/uhcvNwIWIIiugoGXZsmWYPXs2EhMTkZmZiWuuuQaHDh0K194oCMNtxAd8zfjMtBANHxvxabRRq1V4ZNFk/PrKKVCpgDe3VuKON3ai285+SCKKnICClo0bN+Luu+/Gli1bsHbtWjidTixcuBBWK0/PlQvxj4hpiHNa/NcwaCEaPp7TQqPVTecU45nvzYBeq8ba/fVY8uIWtHoPDCUiCrehr2z9rFmzptf7K1asQGZmJnbu3InzzjsvpBuj4FjtwzunBfAvD+OrZUTDJfCcFhrFFk3LQVqCAbe9uh1fVbbhumc349Vb5qAgNT7aWyMihQsoaDmdxeI5dCo1NXXANTabDTabTXq/vZ0n7IZTII34CSFoxBcEAT94bSdsThdevXkO1GrlXMhZuh1Y/OxmVLZ0DbhGp1HjkcsmYcncogjujKJJbMRXzk86UWDmFKfi/TvPxo0vb8PxJisWP7cZr986FxOyEqO9NSJSsKAb8QVBwIMPPoj58+ejtLR0wHXLli2D2WyW3goKCoL9kjQMATXi60feiF/V2o11B+rx+ZEm1Lb3BH0/crTpSBOONHTC5nQP+NZpc+Kd7aeivVWKIGnkMTMtNIqVZCXig7vOwYSsBNS32/Dt5V/i68rWaG+LiBQs6EzLPffcgz179mDTpk2DrnvkkUfw4IMPSu+3t7czcAmjwEYej7ynZU+VRfr/Oks38pLjgr4vudlT3QYAuPbMPDy4cEKfz9e323Dds5txoLYdNqcLBu3Q33OKfexpIfLINhvx7u3zcPMr2/F1ZRuWvLgVz98wC/NL0qO9NSJSoKAyLffeey8++ugjrF+/Hvn5+YOuNRgMSEpK6vVG4eMLWoaOR0MxPUy8sAeAmjZlZVrKvQHZnOJU5KfE93mbUZiM5HgdHC4Bh+s6o7xbihRODyPySY7X441b5+LcknR02V245ZXtWF1eG+1tEZECBRS0CIKAe+65Bx988AE+/fRTFBcXh2tfFKQuW2Qb8cv9Mi21lu6g70du3G4B5dWexzYt39zvGpVKhWl5ns/5B2+kbFIjvoL6t4hGwmTQ4sUbZ+Gyadmwu9y4+62v8M72ymhvi4gUJqCg5e6778Ybb7yBt956C4mJiairq0NdXR26u5VzsRrruhziyOPwN+L7X9gDysq0nGzpQkePE3qtetDm0jJvQLPnlGXANaQsUnlYlPdBJCcGrQZ/v34Gvju7AG4BeOj9cizfeKzftS1WO7Ycb8brX57Ay5sqODaZiIYloJ6WZ599FgCwYMGCXh9fsWIFbrrpplDtiUagyzb88jCpp8UeXNAiXtiLlJRp2VPVBgCYkpMEnWbg2H5aXrJnfTWDltGCjfhE/dOoVVh27TQkx+vx3MZjWLb6IOrbbRiXacKR+k4cquvAkYYONHX2DlKeXHsYt84vxq3nFiPJqIvS7olI7gIKWsRXGEmeHC437C43gEDLw4ILWsQLe5XKUzJTa1FOpkUse5s+QGmYaHqB5/OH6zvQ43DBqGMzvtJJI48ZsxD1oVKp8PCiSUiO1+Hx1Qfx8hcV/a7LS47DhKwE1Fp6cLCuA3/73xG8+uUJ3H7eONx4dtGwXngjotGFzwoKIjbhA5FpxBcnh80sTMGOk62KKg8TH9u0/ORB12UnGZGeYEBTpw37a9sxozAlArujaBJfu2HMQjSwO84fh/QEA17dfAKpJj0mZCWgJCsRE7ISUZKZIL1o5nYLWL23Dk+uPYRjjVY8seYgXtpUgbsvGIfr5xTyhSAikjBoURDxjBatWgW9duh2Jd/I4+Aa8cVsxCVTs7HjZCuaOm2KGP3rcgvYW+N5bGVDZFpUKhXK8s349GADyqssDFpGAakRn6kWokEtnpmPxTMHnzCqVqtweVkOLi3Nxr92VeOpdUdQ2dKFx/69H89/dhw/XjhxyPsgotEh6MMlSX4COaMF8DXiB1Me5n9hf96EDBi8QVK9xRbwfcnN8cZOdNldiNNpMC4jYcj10gSxKva1jAY8p4Uo9DRqFa6dkY///fh8LLt2GnLMRtRaevCT93bjxc+PR3t7RCQDDFoUJJAmfKD34ZKB9iv5X9iPz0xAjtkIAKhRQDO+GHyU5iVBM4yxtmI2ppxjj0cF8TeFmRai0NNp1Lh+TiHW/2QB7r5gHADgdx8fwAdfVUV5Z0QUbQxaFEQsD4sfxrhjwBe0ON0CbE53QF/r9Av7HHMcAGVMEJPOZ/FOBhuKmGk52tA5ooM6KTa4OZCEKOyMOg1+snAibpvvOQ/up/9vDz49WB/lXRFRNDFoURCxPMw03EyL37pAL7bFC/syb6N6TrI306KAZnxxKtpQ/SyizCQjspOMcAvA/tr2MO6M5EBqxGeihSisVCoVfnbZZFx7Zh5cbgF3vfkVdp5sifa2iChKGLQoiBi0xA2zp0WjViFOJx4wGVgz/ukX9rkKybQ4XW7sq/EEHtOGGbT4r2Vfi/K52YhPFDFqtQpPLC7DBRMz0ONw45ZXduBwfUe0t0VEUcCgRUHEQyJNwwxagODOaul1Ye8tjRIzLbUxnmk50tAJm9ONBIMWxWmmYd+uzPt9KPcGc6RkbMQniiSdRo1/LJmBGYXJsHQ78P2XtqGqtSva2yKiCGPQoiBd3sAjkEO5xAliYsAzHOKFfaJBizHeC3tfpiW2g5Zyv14d9TCa8EVSpqWamRal853TwqiFKFLi9Vq8fNNslGQmoK69B99/aRuaO2N/WiURDR+DFgXpcgQ28hgILtPiu7A3Sxf2UqYlxsvD9ngngJUNcajk6cSM0/FGKzp6HCHeFcmJ2IgfQExLRCGQHK/Ha7fOQa7ZiONNVtz8yvagRvaPRFVrFz74qgqPfLAHd76xEyu3VaLFao/oHohGKx4uqSC+kceBBy2BNOL7Lux9PR/i9LDWLge67a5h99XIjRiQiUHIcKUlGJCXHIfqtm7srW7HvHFp4dgeyYA4O4zlYUSRl2OOw2u3zsW3ntuMPVUWXPfMZnxzRh4WTsnC2GGcqxUIQRBwrNGKbRUt2H6iBdsqWlDd1vuFudV76/Dzf+7F3OJULJqWg0umZiEz0RjSfRCRB4MWBZEOlzQEUh4WeNAiXdj7BS1JRi1Meg2sdhdqLd0h/+MRCXanGwdqPQ2ew50c5q8s34zqtm6UV7cxaFEwt1vsaWHUQhQN4zMTsOLmOVj64lYcqu/A46sP4vHVB1GSmYCFU7NwydRsTMszB/w72mV3Yk+VBV9XtuGrylZ8XdmKps7eWRStWoXSPDPmFqfCZNDiv/vrsLe6HZuPNWPzsWb86l97MbsoFZeWZuOKshxkJjGAIQoVBi0KIp3TogumPGx408N6Xdj7nWOiUqmQkxyHow2dqLX0xGTQcri+A3aXG0lGLQpT4wO+/bR8M1bvrcNuThBTNCnTEtVdEI1uZxQkY/1PFmDNvjr8d18dvjzWjCMNnTjS0Il/rD+GHLMRC6dkYVxmAuJ0GsTpNX3+q4IK+2st+OqkJ0g5WNcBl7v3OUwGrRpnFiZjTnEa5han4szC5F59o/ddWILK5i6s3luL1XvrsOtUG7adaMG2Ey34y38P4bVb52JmUUqkvz1EisSgRUGCy7SII4+Hl2kRL+zNcToUpMb1+lyO2YijDZ2oaYvNvhZxXHFZfnJQr6KLQVw5gxZlk85pYdhCFE0ZiQbccFYRbjirCJZuBzYcasAn++qw4VAjai09ePXLkwHfZ3aSETOKkjGjMAVnFiajNM8Mg3bwFwIL0+Jx+/njcPv541DT1o01e+vw7o5TOFjXgZtWbMPbPzgLpQGWHBNRXwxaFKQrmJHH+sDKw3wX9n1T77E+Qay8n16dQIh9MJUtXWjrsiM5Xh+qrZGMsBGfSH7McTpcfUYerj4jDz0OFzYfa8L6g41ottrQZXeh2+5Cj8Pl+X+H5327y42SzAScWZiCGYUpmFGULPVnBis3OQ63zC/Gd+cU4PsvbcOOk634/svb8M4Pz0JJVmKIHi3R6MSgRUHEAyIDaYIPdHqYeGHfX6N6rE8Q8w/IgmGO12FMWjxONHehvNqCc0syQrk9kglfeRijFiI5Muo0+MakLHxjUlbU9hCv1+Llm2dj6YtbsafKgiUvbsW7t8/DmPThn/9FRL1x5LGCiCOPTQGd0xJ8puV0OWZP0FITgwdM9jhcOFTn6dWZFuC4Y3/ibfewREyxxEwLq8OIaDBJRh1evXkOJmYloqHDhiUvbu0zfYyIho9Bi4L4DpcMTyP+UBf2OVJ5WOw9KR+s64DTLSDNpEeuOfhpL2XeDBT7WpRLOlySQQsRDSHFpMfrt83B2HQTqtu6sfTFrWjoiL0X9ojkgEGLggTTiG8KoBF/qAv7XLE8LAYzLeVVbQA8E8BG0mAtjoEur2bQolS+c1oYtRDR0DITjXjjtrnIS45DRZMVS1/cilYeSEkUMAYtCiKNPA4g0yKVh9mHDlqGurAXMy0dNmfMnQovlb2NcMLL1NwkqFRAdVs3mjptodgayYzARnwiClBuchze+sFcZCUZcLi+E99/eRvaY+zvJFG0MWhRECnTEqZG/KEu7E0GLZKMnvuLtQliYmZkJP0sAJBo1GGst9GS2RZlksrD2IhPRAEoSjPhzdvmItWkR3m1BUtf3BqzRwQQRQODFoVwutywOd0AwteIP5wL+9xkT7Yllp6Iu+0uHK73HpgZ5OQwf2Xe7w/7WpTJVx4W1W0QUQwan5mI12+dA3OcDnuqLLji75vwxdGmaG+LKCYwaFEIcXIYENzIY+sQjfjDvbAXJ4jVxVCmZX+tBW4ByEw0ICsp+CZ8kTgOmhPElInTw4hoJKbmmvHve+ZjSk4SWqx23PDSVvxj/VG43cLQNyYaxRi0KES3tzRMo1bBoB3+P6vUiG93SrX6/RnuhX2OmGmJoaBlpOeznK5MasZvC8n9kbywPIyIRqowLR4f3HU2vjUzH24B+NMnh/DD13fA0s0+F6KBMGhRCLG8K16nCWiqkVgeJgi+npj+DPfCXpwqVhtD5WFiGde0vOSQ3N+U3CSoVUB9uw317bETvNHQ/AN7NuIT0UgYdRr86VvT8fi106DXqrHuQAOu/Psm7Kthlp6oP8NvfiBZ8407Hn5pGADE6TRQqwC34Al8TAOMSx7uhb3vrJbYuVjfUx3aTEu8XouSzEQcqu9AeZUFWVNGXnJG8uCfjOTIYyIKhe/OKcTUXDPufHMnKlu6cO0zm/H7b07D4pn50d4aKUCr1Y7nNh7DJ/vqkJloxLjMBIzLMGF8ZgLGZSQgLzkO6hh5FY5Bi0L4JocF9k+qUqlg0mvRYXOi0+ZE5gDrdnvHHQ91YZ/jPaulJkYOmOy0OXGssRMAUDrCccf+puWbcai+A3uqLbhoSlbI7peiy7+AMjae4okoFkzLN2PVvfPxwDu7sOFQI37y3m6sP9iABxdOwLiMhGhvj2KQ1ebEii8qsHzjcXR4q3FONHdh24mWXuuMOjXGpiegLN+MH108ISS9veHCoEUhgjmjRWQy+IKW/nT0OHC8yQrAd3jiQHLFTEtbDwRBkP2r0XurLRAET1lbRqIhZPdblm/G/9tZhT3eYI+Uwd2rPEzeP9tEFFuS4/V4+cbZeHr9Ufx13WF8XF6L1Xtrcd2MfNx3YQkKUuOjvcVRye0WcLKlC3uq2nCiqQuZSQYUpcWjKM2EnCSj7LIUdqcbb2+rxN8/PSqdFzc5Jwn3XDAeTrcbRxs6cayxE0cbOlHRZEWPw439te3YX9uOdQfq8bfvnolzxqdH+VH0L+Cg5bPPPsOf/vQn7Ny5E7W1tfjwww9xzTXXhGFrFAgx0xLIuGOR2Iw/UNCyr6YdggDkJcchPWHwC/tsb09Lt8MFS7cDyfH6gPcTSVLZW4hKw0TiBLHyKktMBG80PAJTLUQURmq1CvddWIILJ2fir2sPY92BBry3swr/3FWN78wuwL3fKAnbK+GdNicqGq043tSJdnEggMozckSl8gwf8fwXMMfpkJMchxyzEekJBmhkduEeLEEQUNXajT1VFuypbkN5lQXl1RZ09PR/faTXqlGQEocxaSYUpsVjUnYirj4jD0Zd4C8gj5TLLeCj3dV4cu1hnGrxVLsUpsbjxwsn4Mqy3H6DK6fLjVOt3Thc34G/rj2Mg3UdWPrSVjxw4QTc843xsvt3DfgK12q1Yvr06bj55ptx3XXXhWNPFASxET+QcceihCHGHvv6WYa+sDfqNEgz6dFstaOmrUf2QYuvnyU5pPc7OScJWrXK832w9CDPO1WNYpubjfhEFAFTc8148cbZ+LqyFU+uPYzPjzThjS2VeG9HFW44qwh3LhiHtCFeRBxIi9WOXadacbzRiuNNVhxv7MTxRisaOmxB3Z9GrUJWogHZZiNykuOQazbi8rJcnFGQHNT9RYogCKhr78HuUxaUV7dhjzdAaevqO8HNoFVjSm4SxmckoLHThsrmLlS2dMHudONYoxXHGq3S2r+tO4KHL5uMK8tyQv6CpSAIaOtyoMbSjdq2HtRaulFr6UGtpQd7qtqkfWQkGnDfhSX4zqwC6AeZKKvVqFGcbkJxugnnT8jArz/ah5XbT+Gv6w5jx8kWPPWdM4L+OQuHgIOWRYsWYdGiRSP/ylYroOnnAlujAYzG3usGolYDcXHBre3qOu1lUz8qFRAfH9za7m7A7R54HyZTUGs//fokshJ0mJrbf+DQ7T2nxWTQAD09gGuQc1fi432HTNhsSIUDcfYefLL1GE6cbOi11GmMw5p9dQCA6ZnGwb/HcXGAWo1ssxHt7VbU1zZhinmAIMq7FgBgtwOOQcY8Go2+n5VA1jocOHSqCZ8fbur3n8+l12Pr8WbPY8uKH/yxGQyA1vvr4nQCtkGe3PV6GHU6TMxOxMGqVjyzajfGpJn6XZqcYsK1c4o9r2a4XJ5/u4HodIDeGwQGstbt9vyshWKtVuv5XgDekXNdoVkbyO99NJ8jHC7E2XsgqE5rxJfBc8SQv/eBrD3tOQLOQQ6fDWRtIL/3EXqOgN0+8Fr/3/tA1g7jOQI6XeBr+Rzhe1+uzxGiEF1HnJmmx+vfLcW2imb8bd0RfFXZhhc3VeCtbZW4cHIWFhQkYP641IGzL97f+/r2Hqz96iTWlddg+4kW9HskjN6I9AQ9xqYnIEsvQO12QwAgQIAgeHr6BMFz4VzvVKOu3Yb6Dhs0djtaG7vR2ggc8N7VG/87gKun53p6JHJSZfEcIajV2HK8BdsO1eLAqRbsrbagubPv77TRYMCEvGRMyzNjenY8pmXEY3xmAnSa3hf/TpcbtT0CTlrsONFsRWVdG/63pwo1jW146NUv8XaBGQ8vmoTpBSmeGwTxHOFwubHzWCM27a3CpqPNqGzukq73RA6NFk6N537NehXuOisfN8wr8vQ427oB/y8zyHOEEcDjl47D3CwjfvPvfdhysBaX/18n/v69MzG7MDm8zxGD/d75E0YAgPDhhx8Ouqanp0ewWCzS26lTpwQAgkX82T/97bLLet9BfHz/6wBBOP/83mvT0wdeO2tW77VFRQOvnTKl99opUwZeW1TUe+2sWQOvTU/vvfb88wdeGx8vLXv9yxPC/8YOcr+A8Mz6o0LRQ6uEB9/ZJQiLFw+6Vujs9O3hxhsHXXvmvW8KRQ+tEooeWiVUf+/mwe+3okIQBEG49ZXtwnNzrh187d69vj08+ujga7dt86394x8HX7t+vW/t008PuvamxY8KRQ+tEoofXiV0PvfC4Pf77ru++3333cHXrlghCIIgPPLBHuGmxYM/tl9cfIfwnz01nvtdv37w+/3jH3172LZt8LWPPupbu3fv4Gt/8hPf2oqKwdfedZdvbUPD4GtvvNG3trNz8LWLF/f+3RhsrQyeIw6lFQqdPQ7f2ig/RwiC4Pm+DPZ98xfC5wihocG39q67Bl/rfY4QBMHzczfYWhk8RwirVvnWrlgx+NogniMEQfB8jcHWPv20by2fI3wGWyuD54hwXUfYUtKEK/7vc+nv8pcFpQOudcXHC89vPCZc+8wXQtFDq4a8jmjrsvv2MMznCKfLLXRdv3TQtS9+sEXotjs99xul54jKNeuFpS9uEYoeWiX8fsHg1zK2det89xul54itv/qLcNcbO4XSR9cMeR2x6oc/E57dcFT4165qoWP12sH3EMBzxCsX3ygUPbRKGPvIx8K7r/xn8Psd4XOEBZ6Y2GKxCIMJeyP+smXL8Nhjj4X7yyjWtooW/PqjfXh+iHXd3kZ8U4Ajj4dy5fQcWJNSUZASj5wPhldHm5ssj8kTHT0OJA7y+fnj05A2Ix9njU2Dqbwx5F//rgXjsH7nQPPYfHaebMWiaTkh//oUXmzEJ6JI02tU+Oiec/BVZSs2HmqE6f2BL+N6HC78/j8HpPfNcbpB73uoz/dHo1YNWZb+j/XHsOKwFb+4fDIuEYSotAPe9/YufJ1dAr1Gjam5SYOu1fdXBRQCq/bU4PwrHbA53XC0dWOwv/rv7jiFj221AIDEAY6iEF1elgucP87zTuvhEO0WuH5OIXZOzcVHu2vw/Gcn8K2Q3XPwVIIgCEHfWKUashHfZrPB5pcCa29vR0FBASw1NUhK6ucHR0Fp3X4FUPpR7VDjqr9vQrPVjlyDCq0d3ZiWb8a7t8/rs/a360/ipU0VuP38sXjkguKoln48u+EYnvy4HNeWZuGJxWWDrgUQttKP9eVVuGvFVozLMGHVfef2XRvl0o8Pv67GT/91ADPGZ3r+TVn64XtfpqUfnTYnZv9uHQQVsOvxa3zNliwPG95alod5sDws8LUx8hwhieB1REtHD7483owvjjThi2ONaGj3/JyqVUDZxDxcWpqNS6ZmI9uAiD5HCIKAj8tr8btPT6LO2y9zbkEifn5pCSZlDxA4hOg5wu0W8OHX1fjrukNo7nTAptXhwtJc/OLyyShK1EXsOWJvtQVPrD6IHSdbYdfq4FJ77lfjdkHvHHgPEwpTcf6UXCyYlInp2QnQOAbZQxifIwSdDm9srcTvPtoLtXfthKwEfHdOIa6cniv1RI/0OaK9vR3m3FxYLJb+YwPxpgPfa2gYDAYYxM35M5l6/4IMZDhrglnr/wQRyrX+T2gjWNttd+GHL21Gs9WOqblJeG7pTHzjLxuwrcGGvW3OPmeK9JoeZgwg02Ew+H54QrQ2N9kIh0aHEz0Y3r+JXu/7YQ/h2t31XejWGzFhbPbQ+9DpfL/0Q9FqfU9SI1g7tSQHTs0R7Ku2wOUWoNFohv8zHMhatTo8a1Wq8KwF5LG2n997t8aBbr3n96tXoiUKzxF9BPJ7H+XnCABh+70PaG0gv/dReI7og88RPnJYK5PriNS4OFyemYLLzxoPQRBwqL4DJ5q6MHtMSvBN1CF4jlABuGJeCb4xsxjPbTiG5Z8dx+enOnDZi1/hrLFpuHhKFi6ekoX8lAG+N0H+3n9d2Ypff7QPu6ssADQYm5eER6+civMnZPRePxwjfI4onWDCayU5WLO3Dn9YfUCa6pWYYESqKQmp8XqkmPTSf8dmmLBgQgYyT+9R0g9zDyF+jlABuOGsIswtTsVLn1fgX7ursbvVid2fHMfvN1Tim2fmYelZRZiY7ff9DOY5YrAA2Q/PaZEhQRDw0/+3G/tq2pFm0uP5789CXnIcLi3Nwb931+DNrZVYdu20XrcZyTktoZYjntViGSSCj4BwjTMOlXEZCYjTaWC1u3C8sRMlWYMVs5Ec+L9QquLMYyKSGZVKhUnZSQNnMqIgXq/Fgwsn4luzCrBs9QH8p7wOm481Y/OxZjz27/2YnJOEi6dkYeGULEzNTQp44pbLLeBAbTt2nGjBF8easXZ/PQDPZNQHLirB9+eNGXSCVripVCosmpaDS6Zmo63bgSSjFlpN9PYTjAlZiXhicRl+dtlk/L+vqvDmlpM43mTF61tO4vUtJzGnOBXfnlWACydlIsUUvqmxAQctnZ2dOHr0qPR+RUUFdu3ahdTUVBQWFoZ0c6PVsxuPYdWeWmjVKjyzZIY0LnfJ3EL8e3cN/rWrGj+7bBISjb7IW8y0xAdxTkuo5XjPaqmzRO+ASUEQ/MYZyzNo0ahVKM1LwvYTrdhTZWHQEgv8gxbGLEREw1aQGo9nlszEyWYr1u6vx3/31WPHyRYcqG3Hgdp2/N//jiAvOQ7nTchAXrIRaQkGpJr0SE/QI9VkQFqCHokGLXocbnx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      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df[df.index<\"2020-05-01\"]['Rt'].replace(np.inf,np.nan).fillna(method='pad').plot(figsize=(10,3))\n",
    "ax.set_ylim([0,6])\n",
    "ax.axhline(1,linestyle='--',color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another interesting indicator of the pandemic is the **derivative**, or **daily difference** in new cases. It allows us to see clearly when pandemic is increasing or declining. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['ninfected'].diff().plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given the fact that there are a lot of fluctuations in data caused by reporting, it makes sense to smooth the curve by running rolling average to get the overall picture. Let's again focus on the first months of the pandemic:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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    "ax=df[df.index<\"2020-06-01\"]['ninfected'].diff().rolling(7).mean().plot()\n",
    "ax.axhline(0,linestyle='-.',color='red')\n",
    "plt.show()"
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    "\n",
    "## Challenge\n",
    "\n",
    "Now it is time for you to play more with the code and data! Here are a few suggestions you can experiment with:\n",
    "* See the spread of the pandemic in different countries.\n",
    "* Plot $R_t$ graphs for several countries on one plot for comparison, or make several plots side-by-side\n",
    "* See how the number of deaths and recoveries correlate with number of infected cases.\n",
    "* Try to find out how long a typical disease lasts by visually correlating infection rate and deaths rate and looking for some anomalies. You may need to look at different countries to find that out.\n",
    "* Calculate the fatality rate and how it changes over time. You may want to take into account the length of the disease in days to shift one time series before doing calculations"
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    "## References\n",
    "\n",
    "You may look at further studies of COVID epidemic spread in the following publications:\n",
    "* [Sliding SIR Model for Rt Estimation during COVID Pandemic](https://soshnikov.com/science/sliding-sir-model-for-rt-estimation/), blog post by [Dmitry Soshnikov](http://soshnikov.com)\n",
    "* T.Petrova, D.Soshnikov, A.Grunin. [Estimation of Time-Dependent Reproduction Number for Global COVID-19 Outbreak](https://www.preprints.org/manuscript/202006.0289/v1). *Preprints* **2020**, 2020060289 (doi: 10.20944/preprints202006.0289.v1)\n",
    "* [Code for the above paper on GitHub](https://github.com/shwars/SlidingSIR)"
   ]
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